Students in the UW ECE Professional Master’s Program, including (from left to right) Bita Massoudi, Jayce Gaddis, and Devika Nair, are learning how to embed advanced AI capabilities into resource-constrained electronics.
In new undergraduate capstone courses taught by UW ECE professors Chris Rudell and Ang Li, students are working together in teams to learn how to design and build microchips for real-world applications.
UW ECE Assistant Professor Yiyue Luo is known for her work in intelligent, or “smart,” textiles. At UW ECE, she and her students are expanding this relatively new line of research into other forms of intelligent wearable technology.
Through a new virtual reality training platform, UW ECE students are gaining immersive cleanroom experience before entering the Washington Nanofabrication Facility for hands-on lab work.
UW ECE doctoral student Malek Itani is being recognized for exceptional early-career research that is advancing the future of information and communication technology.
UW ECE Assistant Professor Hossein Naghavi is leading a research project selected for the U.S. Department of Energy’s Genesis Mission, a historic national initiative aimed at building the world’s most powerful integrated science discovery platform.
Students in the UW ECE Professional Master’s Program, including (from left to right) Bita Massoudi, Jayce Gaddis, and Devika Nair, are learning how to embed advanced AI capabilities into resource-constrained electronics.
In new undergraduate capstone courses taught by UW ECE professors Chris Rudell and Ang Li, students are working together in teams to learn how to design and build microchips for real-world applications.
UW ECE Assistant Professor Yiyue Luo is known for her work in intelligent, or “smart,” textiles. At UW ECE, she and her students are expanding this relatively new line of research into other forms of intelligent wearable technology.
UW ECE doctoral student Malek Itani is being recognized for exceptional early-career research that is advancing the future of information and communication technology.
UW ECE Assistant Professor Hossein Naghavi is leading a research project selected for the U.S. Department of Energy’s Genesis Mission, a historic national initiative aimed at building the world’s most powerful integrated science discovery platform.
Through a new virtual reality training platform, UW ECE students are gaining immersive cleanroom experience before entering the Washington Nanofabrication Facility for hands-on lab work.
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[_finalHTML:protected] => https://www.ece.uw.edu/spotlight/deep-learning-for-embedded-real-time-intelligence/Students build smarter devices with embedded AI
Students in the UW ECE Professional Master’s Program, including (from left to right) Bita Massoudi, Jayce Gaddis, and Devika Nair, are learning how to embed advanced AI capabilities into resource-constrained electronics.
Training the next generation of microchip designers
In new undergraduate capstone courses taught by UW ECE professors Chris Rudell and Ang Li, students are working together in teams to learn how to design and build microchips for real-world applications.
Yiyue Luo — developing intelligent wearable technology for healthcare, robotics and human-computer interaction
UW ECE Assistant Professor Yiyue Luo is known for her work in intelligent, or “smart,” textiles. At UW ECE, she and her students are expanding this relatively new line of research into other forms of intelligent wearable technology.
UW ECE student Malek Itani earns Marconi Society Young Scholar Award for advancing ‘superhuman’ hearing with AI
UW ECE doctoral student Malek Itani is being recognized for exceptional early-career research that is advancing the future of information and communication technology.
UW ECE-led project selected for Department of Energy’s Genesis Mission
UW ECE Assistant Professor Hossein Naghavi is leading a research project selected for the U.S. Department of Energy’s Genesis Mission, a historic national initiative aimed at building the world’s most powerful integrated science discovery platform.
UW ECE introduces virtual reality training for students to help fill crucial semiconductor jobs
Through a new virtual reality training platform, UW ECE students are gaining immersive cleanroom experience before entering the Washington Nanofabrication Facility for hands-on lab work.
Students in the UW ECE Professional Master’s Program, including (from left to right) Bita Massoudi, Jayce Gaddis, and Devika Nair, are learning how to embed advanced AI capabilities into resource-constrained electronics. In the PMP course Deep Learning for Embedded Real Time Intelligence they are taught how to build and deploy AI models that use deep learning in Internet of Things and mobile edge devices.[/caption]
Most Americans today live their lives immersed in technology. Mobile devices, such as smartphones, tablets, and laptops, are common platforms for working, playing, and staying connected with the world around us. Smart televisions, thermostats, and home appliances enabled by the Internet of Things (IoT) make life at home more comfortable and entertaining. And wearable technologies, such as smart watches and rings, help keep us informed about our health and well-being.
Many of these devices rely on machine learning and artificial intelligence (AI) to perform tasks such as recognizing images, understanding speech, and analyzing large amounts of data. And increasingly, these capabilities are powered by deep learning, a subset of machine learning that uses multilayered neural networks to identify complex patterns in data.
This creates a challenge for engineers. As AI becomes ever more integrated into everyday devices, there is a need to develop systems that incorporate deep learning but can still operate efficiently at the network edge, processing data locally on devices that often have limited computing resources, memory, power, and internet connectivity.
Now, students in UW ECE’s Professional Master’s Program are learning how to embed these advanced AI capabilities into resource-constrained electronics. In the course, Deep Learning for Embedded Real Time Intelligence, they learn how to build and deploy AI models that use deep learning in IoT and mobile edge devices.
This course is offered annually during spring quarter and receives generous support from industry partners. For example, during the 2026 spring quarter, students received a starter kit for their final projects from Wyze, a consumer technology company known for developing smart home devices and wireless cameras. Wyze also sent engineers to speak with students about how concepts taught in the course are applied in commercial products.
[caption id="attachment_42275" align="alignleft" width="250"]
UW ECE Professor Richard Shi[/caption]
The course is taught by UW ECE Professor Richard Shi, whose research and teaching focus on embedded systems, AI, machine learning, and hardware-software co-design.
“As AI becomes increasingly embedded in everyday technology, engineers need to understand both machine learning algorithms and the hardware systems that run them,” Shi said. “This course gives students hands-on experience designing, optimizing, and deploying AI solutions for real-world embedded devices, helping them develop skills that are highly relevant across today’s technology industry.”
In addition to being offered through the PMP curriculum, this course is also part of the PMP Certificate Program in Machine Learning and Deep Learning, which provides students with a deep dive into the field.
A common theme echoed throughout the course is that successful AI applications require far more than accurate models. Students must also consider hardware limitations, continuous performance, reliability, and user experience. So, through hands-on labs, industry engagement, and ambitious final projects, students in this course gain practical experience deploying AI in real-world embedded environments.
Three students who completed the course during the 2026 spring quarter shared their experiences and key takeaways:
Bita Massoudi and Devika Nair
[caption id="attachment_42284" align="alignright" width="560"]
PMP student Bita Massoudi demonstrates RespiraEdge, a real-time edge AI system for multimodal respiratory distress monitoring that can run on a laptop.[/caption]
Bita Massoudi is a part-time PMP student who holds a doctoral degree in neuroscience. She has an extensive background in technology development and healthcare, having worked for organizations such as Microsoft and Seattle Children’s Hospital. She also runs her own consulting business. In the PMP, Massoudi is focusing on machine learning and deep learning and anticipates graduating at the end of the 2027 spring quarter.
Devika Nair is a full-time PMP student, who took a break from an established career as a firmware engineer for SanDisk to attend the program. She has in-depth experience working with embedded systems. In the PMP, she is focused on broadening her skillset and integrating her knowledge of real-time embedded systems with machine learning. She also plans to graduate at the end of the 2027 spring quarter.
Massoudi and Nair worked together on their final project in the course, RespiraEdge, a real-time edge AI system for multimodal respiratory distress monitoring.
Why were you interested in this course?
Massoudi: My background is in neuroscience, and I also spent several years in technology development and consulting. I’m especially interested in how AI can be applied to healthcare. But I realized a couple of years ago that there was a gap in my knowledge, especially with how quickly AI and machine learning were developing. That’s what brought me back to school.
I initially was in the Applied Cybersecurity Engineering Certificate Program, and my first course was in TinyML. Once I took that course, I realized that the subject was really interesting to me, and so I switched to the master’s degree program.
The TinyML course was highly hands-on. We learned how to build a complete machine learning system for very small, resource-constrained devices. And for the final project, I built an object detection system using a camera. So, when I saw this course, I was interested because it approached embedded AI from a broader deep learning and real-time system perspective. We went deeper into different neural network architectures, and also into questions of efficiency, such as quantization and latency, memory, and how deep computation can be optimized for different types of hardware.
So, for me, the two courses were complementary. I was able to build on what I had learned in the TinyML course and apply it to very different and more complex applications, and specifically to our final project, RespiraEdge.
"I’d like to combine my background in neuroscience with what I’ve been learning in AI and engineering and work on problems where this technology can have a meaningful, real-world impact." — Bita Massoudi, PMP student
Nair: I was interested in this course because it was one where I felt I could connect my existing background in embedded systems with something I hadn’t worked with as deeply, which was machine learning. One of the things I appreciated most about the course was its hands-on nature. We had labs where we could implement and experiment with concepts, we could write reports, and we were able to see how things behaved, rather than just learning theory.
I would say one of the best things about the course I really want to highlight is that we often heard from guest speakers. We also got the opportunity to visit Wyze, so we could see how some of the ideas we discussed in the course were being applied beyond the classroom.
So, for me, it was the combination of machine learning, embedded systems, and the hands-on practical aspect that really made this course stand out.
What was it like to learn from Professor Shi?
Massoudi: I really enjoyed Professor Shi’s teaching style. He’s hands-on. He would teach the concepts in class, but then we had to apply them and build something that worked. So, it wasn’t just theoretical. He often brought in guest speakers, and he gave us opportunities for site visits, which I really liked because it connected what we were learning in the classroom with how these technologies are being used in industry. That practical side of the course was valuable to me.
Nair: I think the best part of Professor Shi’s course was his energy and enthusiasm throughout the lecture. What really stood out to me is how he emphasized that we needed to get practical experience, and he made sure the course was hands-on. We weren’t just learning concepts from PowerPoints or lectures, we were implementing these concepts through all of the assignments, which were labs. I think Professor Shi provided a very good balance between understanding the fundamentals and actually getting to experiment and build things. And I think that is what made the course so engaging.
Tell me about your final project, RespiraEdge.
[caption id="attachment_42289" align="alignright" width="560"]
A closeup view of the RespiraEdge interactive dashboard, which presents information in an easy-to-understand, visual format.[/caption]
Massoudi: RespiraEdge started with a very simple question: Can we listen to someone’s breathing and use AI to recognize when their breathing pattern becomes abnormal? With my background in neuroscience and interest in healthcare, I was drawn to the idea of whether something as basic as breathing could provide useful information about a person’s condition.
The whole idea is that changes in breathing could be recognized early, and perhaps an AI system could help bring attention to these changes sooner. So, we developed RespiraEdge as a real-time respiratory monitoring system. It listens to breathing sounds and uses deep learning models to determine whether the respiratory pattern appears normal or abnormal.
As the project developed, we wanted the system to look at more than just one prediction, “normal or abnormal.” We added continuous monitoring, heart-rate information from a Bluetooth sensor, and a rule-based intelligent agent that combines respiratory classifications, heart rate, prediction confidence, and trends over time to indicate whether the person appears stable, should continue to be monitored, or may need further attention. We also developed an interactive dashboard that runs locally, so all that information can be presented in an easy-to-understand, visual format.
One of the things that I really liked about this whole system is that everything is processed locally. It doesn’t need to continuously send someone’s respiratory data, that personal information, to the cloud. And the prototype uses relatively inexpensive consumer hardware. It used my computer’s microphone and a heart rate monitor that cost roughly $20.
So, what started as a normal versus abnormal breathing classifier evolved into a complete, real-time monitoring system, which was really cool to see.
"RespiraEdge brought everything together for me. I started out thinking primarily about neural networks, and by the end of the project, I was thinking about the entire system, from the microphone, all the way to what the user sees on the dashboard."
— Devika Nair, PMP student
Nair: RespiraEdge is a device that can continuously listen to a person’s breathing through a microphone, and it can monitor their heart rate. The breathing audio is processed locally and transformed into data that our machine learning model can analyze.
In our case, we used an AI model that classified the respiratory activity as normal or abnormal. We then added the ability to stream the user’s heart rate over Bluetooth. So, we are not relying only on the breathing sound. The system combined those signals, and it displayed a risk state on a live dashboard. So, the main idea of our device was that everything happens locally. We don’t need to send the person’s audio to the cloud.
What I found particularly interesting about this project was that we weren’t just building a classifier, we were trying to make the device behave like a real, continuously running monitoring system. So, we had to deal with things like background noise, false alarms, and we had to make sure the system didn’t constantly switch between states.
Overall, what did you learn from the course?
[caption id="attachment_42291" align="alignright" width="560"]
PMP student Devika Nair explains how RespiraEdge can incorporate heart-rate information from a Bluetooth sensor.[/caption]
Massoudi: The biggest thing I learned was that developing a good AI model is only part of building a real-time intelligent system. You also have to think about computational efficiency, latency, memory, and the hardware. And then when you deploy the device, you have to think about sensors, data, noise, confidence, and reliability through the entire pipeline.
RespiraEdge brought everything together for me. I started out thinking primarily about neural networks, and by the end of the project, I was thinking about the entire system, from the microphone, all the way to what the user sees on the dashboard.
Nair: For me, this course was about the integration of machine learning and embedded systems. Coming from a firmware and embedded systems background, I was already used to thinking about reliability, real-time behavior, and edge cases. But I think this course really helped me see how important those same considerations are when you are building a machine learning system.
Throughout the labs, and especially when working on RespiraEdge, I learned that you can have a model that performs well on your test data, but once you put it into a real environment, you have to think about so many factors, such as noise, data differences, false alarms, and how the system behaves continuously. So, the course changed my perspective from, “Does the model work?” to “Does the whole system in its entirety work reliably?”
How will you use this knowledge going forward?
Massoudi: Going forward, I’d like to continue working at the intersection of AI, engineering, and healthcare. With a background in neuroscience, coming back to school gave me an opportunity to build the machine learning and engineering side of my knowledge. This course, in particular, added another piece to that by teaching me to think beyond just the AI model and more about how you build and deploy a real-time, intelligent system. I’d like to combine my background in neuroscience with what I’ve been learning in AI and engineering and work on problems where this technology can have a meaningful, real-world impact.
Nair: For me, because my background has always been in the same domain, embedded systems, this course has given me confidence and hands-on experience with machine learning as well. I now have a better understanding of how a machine learning model can fit into an embedded real-time system.
In my career, I see myself using these skills, especially at the intersection of embedded systems, firmware, and intelligent systems. So, even if I am not personally developing the machine learning model myself, understanding how the model works, what data it needs, and what it takes to deploy in a real system will help me work more effectively with teams who develop machine learning and AI systems.
Jayce Gaddis
[caption id="attachment_42293" align="alignright" width="560"]
PMP student Jayce Gaddis demonstrates Dog SAURON, a smart, computer-vision-guided laser toy, created to keep his high-energy dog engaged and active.[/caption]
Jayce Gaddis is a part-time PMP student who works at Amazon as a system development engineer. He is part of Amazon Leo, formerly known as Project Kuiper, the company’s low-Earth-orbit satellite internet initiative designed to provide broadband connectivity to underserved communities around the world.
Gaddis specializes in signal processing, analog/mixed-signal hardware design, register-transfer level (RTL) development, and firmware development. He says he is most passionate about developing cutting-edge technologies that make a difference globally. He anticipates graduating at the end of the 2028 spring quarter.
His final course project was a smart, computer-vision-guided laser toy, which he created to keep his high-energy dog engaged and active. He named the project, “Dog SAURON (Smart Autonomous Unit for Redirecting Overstimulated Nonsense),” a tongue-in-cheek title that reflects both the toy’s purpose and outer shell design, which is built to look like the tower of Barad-dûr in Tolkien's classic Lord of the Rings books.
Why were you interested in this course?
Gaddis: Honestly, it sounded really fun. I didn’t know a lot about machine learning or any of these processing algorithms at the time, so it piqued my interest. I saw that it would be an open-ended course where I could work on fun projects that were exciting to me.
What was it like to learn from Professor Shi?
Gaddis: He was a very helpful professor. He had really good insights and focused a lot more on industry, which was also good. It was a different change of pace from other courses that I’ve taken in the program. For example, he brought in some engineers from Wyze to speak a few times because they were applying the same things in their work that we were learning in class. They were able to teach us how they were applying the concepts we were learning, and that was really good.
Tell me about your final project, Dog SAURON.
[caption id="attachment_42295" align="alignright" width="560"]
A photo illustration showing Gaddis's vision for what Dog SAURON could look like after the plastic toy surround is built and a few more features are added. Image provided by Jayce Gaddis.[/caption]
Gaddis: Sure, Dog SAURON is a smart, computer-vision-guided laser toy that I made to keep my dog entertained. I have a Border Collie lab mix, so he has all of the energy and all of the silliness that breed is known for. I had found a laser toy for him on Amazon, and it randomly points a laser around the room for a pet to chase. However, when you have a really smart dog, like a Border Collie, they pick up on the pattern and get bored. So, I got inspired to make something a little bit smarter.
In this course, we were learning about how to use machine learning to find where things are in a frame. And we were also learning about how to classify things, for example, how to tell where the floor is and where the walls are. I saw it as the perfect application to keep my dog engaged. It was a really fun project to work on too.
So, I started developing, building my system on top of the laser toy. I used an Arduino fitted with a camera and shrunk the machine learning model to fit on this little Arduino. That was the most challenging part of the whole project. I tried to change and re-solder the part, but it was cheaply made, and it broke. I really need to make my own circuit board before the device will work 100 percent, but all the necessary algorithms are there.
And then, to move the laser pointer, I used a lot of what I learned from a previous course in control systems. I was able to make a control system loop to guide the laser pointer. I also trained the model to only shine the laser on the floor (rather than on the walls or elsewhere) as a safety feature.
Overall, what did you learn from the course?
Gaddis: I went into this course with only a cursory understanding of AI. I understood the math behind it, at a high level. But I did not know any of the fundamentals that we were taught in this course. Basically, I went in almost blind.
I ended up learning how to train and set up the AI and machine learning models. I also learned that there are different types of transformers to use in different circumstances, and some work better than others. There’s always tradeoffs in this work, so I learned all of that.
"In this course, we were learning about how to use machine learning to find where things are in a frame. And we were also learning about how to classify things, for example, how to tell where the floor is and where the walls are. I saw it as the perfect application to keep my dog engaged. It was a really fun project to work on too." — Jayce Gaddis, PMP student
I also learned how to shrink an AI model down to fit into an embedded system. It’s one thing to run the model on your laptop computer, and a lot of the students were able to do that. It’s a whole other thing to try to shrink that AI model into a smaller, embedded system.
How will you use this knowledge going forward?
[caption id="attachment_42297" align="alignright" width="560"]
A closeup of the microcontroller inside of Dog SAURON. In the background, the furry, high-energy inspiration for this project.[/caption]
Gaddis: In my current job, I don’t do a lot of programming, so I couldn’t see using this knowledge there. But I don’t plan to be in this role forever, and the course gave me a good understanding of other systems and other embedded technologies that I have to work with as a systems engineer, so it built up my strength in that area.
Also, I have to work with the embedded firmware and hardware teams in my current role. This course gave me a better understanding of what is happening when those teams are running these AI models. For example, with the array we are working on, you could use AI to figure out where to steer the antenna beam. So, this course provided me with enough understanding that I could work with them on that, provision appropriate resources, and provide them with the right type of processor for that purpose.
From a larger perspective, the Professional Master’s Program started contributing to my professional goals from the beginning. I moved into the role I have now right after starting in the PMP, and it helped me to adapt really quickly. For example, I took a course in the fundamentals of Maxwell’s equations, and after every class, without fail, I was applying it to my work the next day. I’d be debugging an antenna or an array, and I was able to apply what I learned the day before about reflections, impedance matching, and everything else. The PMP is also flexible enough to allow me to keep working and supporting my family while I’m going to school at night. The program has just been awesome in these ways.
Learn more about the Deep Learning for Embedded Real Time Intelligence course and the UW ECE Professional Master’s Program on the UW ECE website.
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[post_content] => Article by Wayne Gillam, Photos by Ryan Hoover / UW ECE News
[caption id="attachment_42113" align="alignright" width="625"]
In new undergraduate capstone courses offered by UW ECE, students are working together in teams to learn how to design and build microchips for specific, real-world applications. Shown above, chips designed by student teams in the Department’s Integrated Systems Capstone course and VLSI Capstone course.[/caption]
Microchips, also known as semiconductors, integrated circuits, or simply “chips” in engineering circles, are a marvel. These tiny chips may be small, but they punch far above their weight, functioning as the intelligent core of modern technology. Microchips use billions of microscopic transistors with nanometer dimensions to process information, sense the world around us, execute software instructions, provide a platform for communication, and store data. They power a vast array of technologies, ranging from the smartphone in your hand to artificial intelligence to global communication networks for airplanes and satellites.
During the COVID-19 pandemic, when supply chains around the world tightened, it became clear to the United States government how important microchips are to the economy and national security. This realization launched an ongoing effort to revitalize semiconductor design and manufacturing across the country. A crucial part of this initiative is workforce development — training the next generation of engineers who can design and build microchips.
UW ECE is helping to meet this important need in several different ways, including participating in programs aimed at semiconductor workforce development, such as UPWARDS for the Future, and by providing virtual reality training for students.
“I would like to emphasize that we are providing pathways for a student to graduate with a bachelor’s degree and pursue a viable career in integrated circuit design. Traditionally, this option was only available to those with master’s and doctoral degrees.”
— UW ECE Professor Chris Rudell
Now, in new undergraduate capstone courses taught by UW ECE professors Chris Rudell and Ang Li, students are working together in teams to learn how to design and build microchips for specific applications. In this rigorous curriculum, students not only design chips, but over the span of about a year, they have the opportunity to send their designs out for fabrication at an industrial foundry and follow up with testing to ensure their chips work in real-world settings.
“It is clear that the demand for chip designers has risen, particularly in the United States. This is a critical technology for defense applications, economic development, and high tech,” Rudell said. “Nationwide, we’re just not graduating enough students that have skills to do integrated circuit design. That’s the critical need our courses are helping to address.”
“These courses provide a realistic and comprehensive experience for students interested in the semiconductor design industry,” Li added. “We teach students valuable design skills as well as system understanding, testing, and post-silicon practices.”
A collaborative environment provides uncommon opportunities
[caption id="attachment_42115" align="alignright" width="525"]
A closeup view of a chip designed by UW ECE doctoral student Elpida Karapepera and her student teammates in the 2025 spring quarter Integrated Systems Capstone course. This receiver chip significantly improves signal linearity and filters out strong interference. It is designed for advanced wireless communication applications, including Wi-Fi, Bluetooth, and software-defined radios. The chip design was described in a paper published at the 2026 IEEE International NEWCAS Conference. Karapepera, who now is a teaching assistant for the capstone course, received a Best Student Paper Award at the Conference for this work.[/caption]
These courses got their start at UW ECE in 2025 and receive support from the Center for Education of Microchip Designers, or CEMiD, which is based at the University of California, Los Angeles (UCLA). CEMiD, led by professor Behzad Razavi at UCLA, works with other universities across the nation to train students to become microchip designers. Further support for Rudell and Li’s courses is provided by Apple, Advanced Micro Devices (AMD), Muse Semiconductor, and Taiwan Semiconductor Manufacturing Company (TSMC), the world’s largest dedicated semiconductor foundry.
As one of eight universities participating in CEMiD’s pilot program in 2025, UW ECE stands out as a pioneer in developing comprehensive chip tapeout courses like these for undergraduate and graduate students. The term “tapeout” comes from the early days of semiconductor design and fabrication, when the data file containing every detail of a chip’s physical definition was loaded onto a magnetic reel and shipped in the fastest way possible to a chip fabrication facility. Today, this same file that signifies the completed chip design is transferred over the internet. The tapeout is the final stage of the chip design process, when a completed and verified digital design is sent to a foundry to be manufactured into a silicon chip.
For undergraduate students at UW ECE, there are two main avenues for studying chip design. One is the Integrated Systems Pathway, which provides a sequence of courses that culminate in the Integrated Systems Capstone, the analog, mixed-signal, and radio frequency (RF) chip tapeout course taught by Rudell and other professors at UW ECE, depending on the academic year. The other is the Very Large-Scale Integrated (VLSI) / Digital Systems Design Pathway, which puts students on track to take the VLSI Capstone, the digital chip tapeout course taught by Li. The capstone courses are cross-listed, so graduate students and those from other UW schools and departments can also have this educational opportunity.
[caption id="attachment_42117" align="alignleft" width="525"]
UW ECE Professor Chris Rudell (right) instructing students in the Integrated Systems Capstone course. This undergraduate capstone course teaches students how to design analog, mixed-signal, and radio frequency (RF) chips to an industry-level standard.[/caption]
“As an undergraduate, there aren’t many opportunities to tape out a chip, let alone get the experience of designing a chip from start to finish,” said UW ECE alumnus Bryan Kim (BSECE ‘26), who took Rudell’s chip tapeout course during his senior year. “On top of that, by this point in the Pathway, the students who have stuck with it tend to be more motivated, which makes the group work more enjoyable.”
Students in both capstone courses can follow their designs through fabrication, which happens in the summer, and continues with testing and verification during the following academic year. Students who choose this option participate in a year-long experience that culminates in the design, fabrication, and testing of a functional microchip built for a specific application.
“I would like to emphasize that we are providing pathways for a student to graduate with a bachelor’s degree and pursue a viable career in integrated circuit design,” Rudell said. “Traditionally, this option was only available to those with master’s and doctoral degrees. The barrier to entry for a career in microchip design is quite high, but these courses shatter that obstacle for students by providing a real-world design experience.”
Advanced chip design, with real-world applications
[caption id="attachment_42119" align="alignright" width="525"]
A closeup view of a chip designed by students in the VLSI Capstone course. This chip, titled “Bluetiful” by the student team that designed it, is a digital-only Bluetooth Low-Energy, or BLE, transmitter chip based on patented intellectual property from the lab of UW ECE Professor Matt Reynolds. The chip design was described in a paper published at IEEE RFID 2026, demonstrating UW ECE students working at a high level.[/caption]
The learning experience extends well beyond the classroom. In the tapeout courses, student teams are encouraged to turn their own ideas into microchip designs, and they are provided with a robust set of resources to do so. In addition to guidance from the instructors and teaching assistants, engineers from Apple and AMD meet regularly with students to evaluate chip designs and offer feedback. Students are also offered the chance to collaborate with UW faculty members, developing microchips for research projects.
Students in the Integrated Systems Pathway have been advised by UW professors Robert Miyaoki, Hossein Naghavi, Sajjad Moazeni, and industry collaborators at Boeing, Amazon, and Texas Instruments. Students in the VLSI Digital Systems Design Pathway have also been advised by UW ECE professors.
“Last year, there were two student teams working with professor Matt Reynolds on Bluetooth transmitters,” Li said. “This year, we have two teams working with professor Yiyue Luo, building an AI accelerator for her intelligent textile glove. And we have yet another team collaborating with professor Lih-Yuan Lin on using micro-LED [light-emitting diodes] to do data transmission.”
[caption id="attachment_42121" align="alignleft" width="525"]
UW ECE Assistant Professor Ang Li (standing, second from right) advises students in the VLSI Capstone course. This undergraduate capstone course teaches students how to design and build industry-standard digital microchips for modern technology.[/caption]
As a result of this intellectual freedom, strong academic-industry partnership, and highly collaborative environment, many of the students’ chip designs are quite advanced. Some projects have even progressed beyond the classroom and into peer-reviewed publications and presentations at leading engineering conferences, such as the 2026 Institute of Electrical and Electronics Engineers (IEEE) International New Circuits and Systems (NEWCAS) Conference, the 2026 IEEE International Conference on Radio Frequency Identification (RFID 2026), and the 2026 IEEE Radio Frequency Integrated Circuits (RFIC) Symposium.
“Even after the course ended in Spring 2025, we continued testing the chip when it returned from fabrication to the Department in Autumn 2025,” said UW ECE alumna Kate Tseng (BSECE ‘26), who took Li’s chip tapeout course during her junior year. “Since the chip was successful, we were able to write a manuscript during the winter quarter and publish our paper at IEEE RFID 2026.”
“I’m excited to share that our work was accepted by the 2026 IEEE RFIC Symposium, proving that projects in this course reach a true professional publication standard,” said Zhuoran Wu (MSEE ‘26), an alumnus and incoming doctoral student who took Rudell’s chip tapeout course while completing his master’s degree. He added, “The most important thing this course taught me is how to make sure a design actually works when it is fabricated in silicon, not just on a computer screen.”
Looking ahead
[caption id="attachment_42123" align="alignright" width="525"]
Students in the undergraduate VLSI Capstone course testing a field-programmable gate array (FPGA) prototype of their chip design before sending it out for fabrication.[/caption]
Going forward, Rudell said that he anticipates these capstone courses will be taught by a core group of faculty in the Department who are experts in different aspects of chip design, rotating from year to year and offering students a range of expertise along with a variety of experiences. He and Li are also facilitating a passing down of knowledge between generations of students. For example, they ask students from prior years to provide a presentation on their course experience to students in the current year. And they are continually integrating feedback from students to improve course infrastructure.
Rudell and Li also said that they look forward to growing industry partnerships and support for these chip tapeout courses as they help to meet the demand for qualified microchip designers.
“We are producing competitive engineers at UW ECE,” Li said. “Only very few schools and universities offer this type of curriculum, and it’s not just because of the need for funding, it’s also because of the effort required. It takes more than a year to train a cohort of students, and we are doing that every year. The Department is dedicated to this continuous, non-stop effort because we all realize how important it is.”
“This course provides an excellent platform to learn about something that is so critical to economic development, national defense, and high-performance technology applications,” Rudell said. “For most companies, what they are seeking are college graduates that have been trained in this area and know how to leverage the most advanced CAD [Computer-Aided Design] tools to design a chip. That is exactly what we are providing.”
The future looks bright for those who want to build an academic foundation at the UW for a career in integrated circuit design. By the time they graduate, UW ECE students are doing far more than simply learning about semiconductor technology. They are designing, fabricating, and testing real microchips, gaining valuable experience that prepares them well for a career in the semiconductor industry.
The undergraduate Integrated Systems Capstone course is offered during winter and spring quarters at UW ECE, and the undergraduate VLSI Capstone course is offered in spring quarter. For more information, contact UW ECE professors Chris Rudell and Ang Li.
[post_title] => Training the next generation of microchip designers
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[post_content] => Article by Wayne Gillam, Photos by Ryan Hoover / UW ECE News
[caption id="attachment_42076" align="alignright" width="575"]
UW ECE Assistant Professor Yiyue Luo is known for her work in intelligent, or “smart,” textiles. At UW ECE, she and her students are expanding this relatively new line of research into developing other forms of intelligent wearable technology.[/caption]
UW ECE Assistant Professor Yiyue Luo found her professional focus in a surprising way: by choosing to avoid things she didn’t like.
Today, Luo leads the Wearable Intelligence Lab at the UW, where she develops intelligent wearable technologies that can monitor health, improve rehabilitation, and provide robots with a more human-like sense of touch.
“It’s easy for me to realize what I don’t like,” Luo said. “I try not to do what I don’t like, and then that choice tends to move me toward something that is a much better fit.”
This approach to life is reflected in how she first became involved in electrical and computer engineering.
Luo said she was interested in science and engineering from a young age, but in high school, she wasn’t sure which field she wanted to pursue. Her father, a materials scientist, encouraged her to follow in his footsteps.
Taking her father’s advice, Luo enrolled in materials science and engineering at the University of Illinois Urbana-Champaign. As she progressed through the program, however, she discovered that her interests were shifting. Undergraduate research experiences exposed her to electronics design, and she became increasingly drawn toward electrical engineering.
“The sense of touch is a physically grounded modality that humans take for granted. Information from tactile sensing is extremely valuable for many applications, but collecting it remains challenging because the necessary hardware often does not exist. In my lab, we are creating this needed hardware in new form factors that can capture tactile information in a scalable, comprehensive, and continuous way.” — UW ECE Assistant Professor Yiyue Luo
She had already begun participating in materials science research as an undergraduate, but during her sophomore year she decided to change direction and join the Rogers Research Group. This research group was led by Professor John A. Rogers (now at Northwestern University), who was well-known for his interdisciplinary work in materials science, electronics, and bioengineering.
[caption id="attachment_42080" align="alignright" width="475"]
UW ECE postdoctoral scholar and Entrepreneurial Fellow Sen Zhang (left) with Luo in the Wearable Intelligence Lab. Luo is trying on a MultiSensKnit prototype, a sensor-packed knitted sleeve she developed with Zhang that is designed for rehabilitation assessment.[/caption]
At the time, Rogers was exploring epidermal electronics, ultra-thin, flexible electronic devices that stick to human skin like a temporary tattoo. These devices can monitor physiological signals, serve as highly sensitive electronic interfaces, and even deliver targeted therapies. Luo said she found the research fascinating, and it helped to lay a foundation for her later work.
The experience convinced Luo that the future she wanted to pursue was at the intersection of electronics, computing, and human-centered applications.
After receiving her bachelor’s degree in materials science and engineering, Luo decided to pursue a doctoral degree in electrical engineering and computer science. The transition was unconventional because her undergraduate training was in materials science rather than electrical engineering. But for Luo, the decision felt like a natural progression toward the work that excited her most.
She was accepted into MIT’s EECS program, where she was advised by professors Wojciech Matusik and Tomás Palacios.
It was in Matusik’s Computational Design & Fabrication Group where Luo found her true calling. There, she was introduced to a digital knitting machine that transforms digital designs into knitted fabrics. Matusik and his students used the machine to knit conductive yarn into garments, creating a type of wearable technology known as “intelligent textiles” — fabrics embedded with sensors, actuators, and conductive fibers.
[caption id="attachment_42083" align="alignleft" width="475"]
Luo adjusting the digital knitting machine in the Wearable Intelligence Lab. This device transforms digital designs into professional-quality, knitted fabrics.[/caption]
Intelligent textiles, also known as “smart textiles,” can sense, store, process, and communicate information seamlessly and unobtrusively. They have a wide range of potential applications, including monitoring physiological signals and acting as control interfaces for electronic devices.
As she began working with these textile-based sensing systems, Luo realized that fabric offered a uniquely powerful form factor because of its ubiquity. People wear clothing every day and constantly encounter fabrics through furniture, carpets, and bedding. She also recognized the potential these technologies had to capture tactile information that cameras, microphones, and conventional electronics cannot easily detect.
“With smart textiles, we can sense, monitor, and embed important, physically grounded information, well beyond what images, audio, and text can provide,” Luo said. “This information can be fundamental for understanding human behavior and activity, so I think a lot of opportunities emerge from that.”
While at MIT, Luo produced cutting-edge wearable textile technologies, including digitally embroidered smart gloves that can record, reproduce, and transfer tactile interactions. These gloves can help users learn complex physical hand movements and can enable touch-sensitive robot teleoperation. She also created AI-powered sensing textiles — including vests, socks, and gloves — that can record and monitor a person’s physical interactions with their environment in real time. The technology has a wide range of potential applications in healthcare and robotics. KnitUI, another system she developed, is a machine-knitted user interface that allows users to operate devices through pressure-sensitive fabric.
“I believe students need to experience things hands-on. That’s how I learned, especially as an undergraduate. I feel like I didn’t truly understand research until I actually touched the textile samples and gained a tangible understanding of how things worked. I think this is an important part of my research and my teaching philosophy.” — UW ECE Assistant Professor Yiyue Luo
Luo’s research has been published in leading interdisciplinary journals, such as Nature Electronics, and at top human-computer interaction and robotics conferences. Her work has also been featured in major media outlets and exhibitions.
Her contributions have earned numerous honors, including Best Paper at the 2025 ACM Conference on Human Factors in Computing Systems (CHI) and Best Paper Honorable Mentions at the 2025 ACM Symposium on User Interface Software and Technology (UIST) and CHI 2021. Additional recognitions include the 2025 Sony Faculty Innovation Award, the MIT EECS Jin Au Kong PhD Thesis Award, and selection to Forbes’ 2024 30 under 30 North America list in Science.
By the time she completed her doctorate, Luo had established herself as a rising researcher in wearable intelligence and smart textiles. The next step was building a research program of her own.
After receiving her doctoral degree from MIT, Luo joined UW ECE in September 2024 as an assistant professor.
“UW ECE is special in that it’s technical and rigorous, but it’s also open-minded,” Luo said. “It has faculty with deep fundamental knowledge about electronics, and it provides a supportive, interdisciplinary environment. This all allows me to collaborate on impactful projects that have potential for major contributions to science and technology.”
The Wearable Intelligence Lab
[caption id="attachment_42089" align="alignright" width="475"]
Luo and her students use the digital knitting machine in her lab to knit together traditional yarns (colorful spools) with conductive yarn (gray spool in the foreground), creating fabric that is the basis for different types of intelligent wearable technology.[/caption]
At the UW, Luo established the Wearable Intelligence Lab to advance the integration of wearable technology and artificial intelligence into end-to-end systems for healthcare, robotics, and human-computer interaction.
Under Luo’s guidance, students in the lab are developing new approaches to tactile sensing and wearable intelligence. UW ECE doctoral student Devin Murphy has developed a smart glove with a sense of touch that could support rehabilitation medicine and help teach robots tactile sensing capabilities. UW ECE doctoral student Hongyu Mao and postdoctoral Entrepreneurial Fellow Sen Zhang are working with Luo to develop MultiSensKnit, a sensor-packed knitted sleeve designed for rehabilitation assessment. Another UW ECE doctoral student, Chankyu (Charlie) Han, has developed MagBall, a magnetic-ball sensor capable of capturing subtle interactions across glass, metal, and human skin. Other lab projects include a glove that teaches the user CPR, machine-knitted magnetoactive textiles that provide sensing and haptic feedback for various applications, and sensorimotor stickies, small patches that can deliver localized, on-body sensing and haptic cues in real time.
“The sense of touch is a physically grounded modality that humans take for granted. Information from tactile sensing is extremely valuable for many applications, but collecting it remains challenging because the necessary hardware often does not exist,” Luo said. “In my lab, we are creating this needed hardware in new form factors that can capture tactile information in a scalable, comprehensive, and continuous way.”
[caption id="attachment_42091" align="alignleft" width="475"]
Luo demonstrates a digitally machine-knitted assistive glove, designed to support hand movement.[/caption]
Luo’s research is interdisciplinary by nature.
At the UW, she collaborates with faculty and students across multiple disciplines to explore new applications for wearable technology and tactile sensing. She is working with UW ECE Professor Chet Moritz, who holds joint appointments in UW Medicine, on rehabilitation technologies, including the MultiSensKnit sleeve she is developing with Zhang. She has also collaborated with UW ECE Associate Professor Sam Burden on equipping robots with human-like sensing capabilities for manipulation tasks and with Siddhartha (Sidd) Srinivasa, a professor in the Paul G. Allen School of Computer Science & Engineering, on human-computer interaction. Beyond engineering and computer science, Luo and UW ECE doctoral student Devin Murphy are brainstorming new tactile-sensing applications with UW Assistant Professor Meichun Liu and her industrial design students. Luo also provides digital knitting machine workshops in the Allen School’s Fabrication Research Lab, helping to introduce students from a variety of disciplines to wearable computing and textile-based technologies.
As an educator, Luo instructs and mentors a mix of undergraduate and graduate students. She said she enjoys teaching and learning from her students as well as watching them develop confidence in their independent thinking. To help facilitate that growth, she encourages students to remain open-minded and pursue experiential learning alongside their academic coursework.
“I believe students need to experience things hands-on,” Luo said. “That’s how I learned, especially as an undergraduate. I feel like I didn’t truly understand research until I actually touched the textile samples and gained a tangible understanding of how things worked. I think this is an important part of my research and my teaching philosophy.”
For more information about UW ECE Assistant Professor Yiyue Luo, visit her bio page and the Wearable Intelligence Lab website.
[post_title] => Yiyue Luo — developing intelligent wearable technology for healthcare, robotics and human-computer interaction
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[post_content] => Adapted from an article by Kristin Osborne / Paul G. Allen School of Computer Science & Engineering
[caption id="attachment_41949" align="alignright" width="525"]
UW ECE doctoral student Malek Itani has received a 2026 Marconi Society Paul Baran Young Scholar Award for exceptional early-career research that is advancing the future of information and communication technology.[/caption]
In 2022, UW ECE doctoral student Malek Itani purchased his first pair of Apple Airpods Pro in a pre-holiday sale. When he put them in his ears, something clicked — and it wasn’t a sound, but rather an idea.
“I put them on my ears, and I turned on noise canceling, and suddenly I felt like I was in my own personal space,” said Itani, a research assistant in the Mobile Intelligence Lab led by Allen School professor Shyam Gollakota. “I thought, ‘Wow, we can do something here.’
“But the model I was working on at the time was kind of huge, and not real-time, and definitely not something you can put on earbuds,” he continued. “And Shyam said, ‘But what if you can?’ “
Itani embraced the challenge. And after four years of steady and, at times, astounding progress, he received a Marconi Society Paul Baran Young Scholar Award for exceptional early-career research that is advancing the future of information and communications technology (ICT). Itani is one of only three scholars selected from a record-high number of nominees from around the world; he and his fellow honorees will be formally recognized at the Marconi Awards Gala & Institute Forums November 4-6 in San Francisco, California.
“Malek has been a key part of every major contribution to the field of superhuman hearing in recent years,” said Gollakota. “He entered his Ph.D. with a background in RF and backscatter, but he rapidly mastered audio signal processing and deep learning, which is very impressive.”
As an undergraduate, Itani was eager to explore different areas of his chosen field. He dabbled in the aforementioned radiofrequency (RF) communication, embedded systems, robotics and even competitive programming — all the while resisting well-meaning suggestions that he specialize. That breadth of experience was an asset in Gollakota’s lab, where the research is cross-disciplinary and the members approach problems from different, sometimes unexpected, angles.
Itani embodied this ethos during his first foray into the soundscape, which focused not on in-ear capabilities but around-the-room. In his first paper as a primary author, Itani and co-primary author Tuochao Chen, a Ph.D. student in the Allen School, introduced acoustic swarms, a system that creates speech zones in a room by tracking and separating multiple speakers simultaneously. The system consists of a neural network paired with a set of small robotic microphones that self-distribute across a space using only sound — no cameras or special substrate required. The robots automatically return to their charging station after deployment, making the system portable and easy to set up in new locations.
As it turned out, the project was Itani’s ideal introduction to his new line of research.
“The transition from RF to audio is actually simple, because you work with waves and frequencies — but instead of looking at gigahertz, you’re now looking at kilohertz,” Itani explained, “In some sense, it’s easier working with sound, because there’s less data to process. And it’s also more fun to work with, because you get to hear the end product.”
It was when he teamed up with another labmate, Bandhav Veluri (Ph.D., ‘25), on a project called Waveformer that he began to embrace this new direction.
“I had a lot of background in embedded systems because of my undergraduate work and because of the robots,” Itani said. “I was able to take that and port it over to an embedded system, run it in real time, and integrate it with the noise-cancelling headsets. That’s where I started to really learn about real-time audio processing.”
"I feel like I’ve hit a niche that’s going to be so transformational, and it’s going to be in billions of devices. It’s going to change the way we hear the world." — UW ECE doctoral student Malek Itani, 2026 Paul Baran Young Scholar
The result was the first neural network capable of real-time, streaming target sound extraction, which the researchers then translated into semantic hearing. Using off-the-shelf headphones paired with a smartphone, Itani and Veluri created a system that enabled the wearer to tailor what sounds they hear in their environment. For example, a person could program the device so that they could hear bird song while walking in the park but not the sound of nearby traffic. A subsequent project, target speech hearing, enabled wearers to focus on the voice of a single companion in a crowd simply by looking at them. The system leverages AI to learn and latch onto the target person’s speech patterns, which it plays back to the wearer in real time while canceling out other voices.
Itani and Chen then extended the wearer’s control over their soundscape from selected sounds to a selected space with a prototype headset that enabled the wearer to create a sound bubble. All sounds within the bubble’s perimeter are heard clearly; sounds outside the bubble are muffled or silenced. An onboard neural network determines which sounds to amplify or suppress based on the distance of each source from the embedded microphones.
That successful proof of concept inspired Itani to aim smaller and refine the technology for earbuds and hearing aids.
“Hearing aids are a natural use case,” Itani said. “In a noisy environment, hearing aids will amplify everything, but if you use AI you can amplify specific sounds that people care about. And you can recover not only what they would have heard, but you can also recover things that humans normally can’t hear. That’s where the concept of superhuman hearing comes from — you’re extending what’s possible with normal hearing.”
But this use case required the team to incorporate AI into devices with significant power and processing constraints. Last year, Itani, Chen and Gollakota partially answered that question with the development of TF-MLPNet, the first real-time neural speech separation network capable of running on low-power hearables like earbuds and hearing aids. They achieved another first with the introduction of NeuralAids, a programmable on-device AI platform for wireless hearables that achieves real-time speech enhancement under strict power constraints.
It wasn’t long before the team’s progress attracted the attention of industry. The team co-founded a UW startup, Hearvana AI, which raised $6 million last fall to support their push to bring acoustic intelligence to market.
As for what happens next, Itani says to stay tuned.
“I feel like I’ve hit a niche that’s going to be so transformational, and it’s going to be in billions of devices. It’s going to change the way we hear the world,” Itani said. ”Because of how important this is going to be, and how much this is going to change people’s lives, it genuinely feels like I have this responsibility to push this forward. I get to impact many, many people with this.”
To learn more, read the Marconi Society announcement and Itani’s Young Scholar profile, and visit Itani’s personal website.
[post_title] => UW ECE student Malek Itani earns Marconi Society Young Scholar Award for advancing ‘superhuman’ hearing with AI
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[post_content] => By Wayne Gillam / UW ECE News
[caption id="attachment_41758" align="alignright" width="580"]
UW ECE Assistant Professor Hossein Naghavi is leading one of 278 research projects chosen for the U.S. Department of Energy’s Genesis Mission from more than 5,000 applicants nationwide. His project is focused on developing an artificially intelligent augmented reality headset that would enable the user to see through smoke, fog, debris, and other nonconductive materials. Photo by Ryan Hoover / UW ECE[/caption]
UW ECE Assistant Professor Hossein Naghavi is leading a multi-institutional research project selected for the U.S. Department of Energy’s Genesis Mission, a historic national initiative aimed at building the world’s most powerful integrated science discovery platform. His project is one of only 278 selected nationwide from more than 5,000 applicants, the largest response to a funding opportunity in DOE history. The selected projects were announced on July 22 at the Genesis Mission Summit in Washington, D.C.
Naghavi’s project, “Neuromorphic Terahertz Imaging via Analog Compute-in-Memory in AI-Driven Augmented Reality Hardware,” is focused on developing a low-power, high-bandwidth augmented reality headset that combines terahertz imaging with intelligent sensing and computing. Terahertz waves sit on the electromagnetic spectrum between microwave and optical frequencies. They can be used to see through many nonconductive materials and identify substances based on unique wave absorption and reflection signatures. The headset would enable the user to see through smoke, fog, debris, and other nonconductive matter. Potential applications include firefighting, emergency response, autonomous navigation, security screening, industrial inspection, biomedical sensing, and beyond 5G communication networks.
“I am honored to represent the University of Washington as part of the DOE’s Genesis Mission,” Naghavi said. “This is an exciting project that is rethinking how intelligent sensors are built, and by doing so, the research is supporting national priorities in energy-efficient computing and next-generation hardware.”
According to the DOE, the Genesis Mission was designed to address some of the nation’s most pressing energy, scientific, and engineering challenges while doubling America’s scientific productivity. By uniting government, industry, academia, and philanthropy, the initiative accelerates breakthroughs in energy, scientific discovery, and national security through a new platform combining AI, supercomputing, quantum systems, and advanced scientific instruments.
[caption id="attachment_41763" align="alignleft" width="430"]
The U.S. Department of Energy’s Genesis Mission is a historic national initiative aimed at building the world’s most powerful integrated science discovery platform.[/caption]
Projects under the Genesis Mission are collaborative by design; teams must draw on the expertise of researchers from academia, industry, and/or national laboratories. Naghavi’s co-investigators include Milad Koohi, an assistant professor of electrical and computer engineering at Texas A&M University, Morteza Fayazi, an assistant professor of electrical and computer engineering at the University of Utah, and Daniel Elmhurst, chief executive officer of ChipNexus (formerly PrimisAI). The group is also collaborating with John Josephakis, global vice president of high-performance computing and supercomputing at Nvidia.
Naghavi and his team have been selected by the DOE under the Genesis Mission for Phase I funding. During this nine-month phase, the team will design and demonstrate a research workflow that integrates AI with scientific investigation. The DOE will evaluate whether the approach can accelerate discovery, improve predictive capabilities, enhance experimentation, and generate new scientific insights. Projects demonstrating strong potential for transformative scientific capabilities may be considered for additional Genesis Mission funding.
Naghavi said that the nine-month timeframe was ambitious, but he and his colleagues were up to the challenge.
“This project brings together expertise in terahertz systems, semiconductor devices, integrated microsystems, AI methods/hardware, and high-performance computing,” Naghavi said. “By combining those strengths, we can move much faster toward a practical solution than any one institution could alone.”
Read this DOE press release to learn more about the first Genesis Mission projects selected to accelerate AI-driven scientific discovery.
[post_title] => UW ECE-led project selected for Department of Energy’s Genesis Mission
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[post_title] => UW ECE introduces virtual reality training for students to help fill crucial semiconductor jobs
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[post_content] => Article by Wayne Gillam / Photos by Ryan Hoover
[caption id="attachment_42273" align="alignright" width="660"] Students in the UW ECE Professional Master’s Program, including (from left to right) Bita Massoudi, Jayce Gaddis, and Devika Nair, are learning how to embed advanced AI capabilities into resource-constrained electronics. In the PMP course Deep Learning for Embedded Real Time Intelligence they are taught how to build and deploy AI models that use deep learning in Internet of Things and mobile edge devices.[/caption]
Most Americans today live their lives immersed in technology. Mobile devices, such as smartphones, tablets, and laptops, are common platforms for working, playing, and staying connected with the world around us. Smart televisions, thermostats, and home appliances enabled by the Internet of Things (IoT) make life at home more comfortable and entertaining. And wearable technologies, such as smart watches and rings, help keep us informed about our health and well-being.
Many of these devices rely on machine learning and artificial intelligence (AI) to perform tasks such as recognizing images, understanding speech, and analyzing large amounts of data. And increasingly, these capabilities are powered by deep learning, a subset of machine learning that uses multilayered neural networks to identify complex patterns in data.
This creates a challenge for engineers. As AI becomes ever more integrated into everyday devices, there is a need to develop systems that incorporate deep learning but can still operate efficiently at the network edge, processing data locally on devices that often have limited computing resources, memory, power, and internet connectivity.
Now, students in UW ECE’s Professional Master’s Program are learning how to embed these advanced AI capabilities into resource-constrained electronics. In the course, Deep Learning for Embedded Real Time Intelligence, they learn how to build and deploy AI models that use deep learning in IoT and mobile edge devices.
This course is offered annually during spring quarter and receives generous support from industry partners. For example, during the 2026 spring quarter, students received a starter kit for their final projects from Wyze, a consumer technology company known for developing smart home devices and wireless cameras. Wyze also sent engineers to speak with students about how concepts taught in the course are applied in commercial products.
[caption id="attachment_42275" align="alignleft" width="250"] UW ECE Professor Richard Shi[/caption]
The course is taught by UW ECE Professor Richard Shi, whose research and teaching focus on embedded systems, AI, machine learning, and hardware-software co-design.
“As AI becomes increasingly embedded in everyday technology, engineers need to understand both machine learning algorithms and the hardware systems that run them,” Shi said. “This course gives students hands-on experience designing, optimizing, and deploying AI solutions for real-world embedded devices, helping them develop skills that are highly relevant across today’s technology industry.”
In addition to being offered through the PMP curriculum, this course is also part of the PMP Certificate Program in Machine Learning and Deep Learning, which provides students with a deep dive into the field.
A common theme echoed throughout the course is that successful AI applications require far more than accurate models. Students must also consider hardware limitations, continuous performance, reliability, and user experience. So, through hands-on labs, industry engagement, and ambitious final projects, students in this course gain practical experience deploying AI in real-world embedded environments.
Three students who completed the course during the 2026 spring quarter shared their experiences and key takeaways:
Bita Massoudi and Devika Nair
[caption id="attachment_42284" align="alignright" width="560"] PMP student Bita Massoudi demonstrates RespiraEdge, a real-time edge AI system for multimodal respiratory distress monitoring that can run on a laptop.[/caption]
Bita Massoudi is a part-time PMP student who holds a doctoral degree in neuroscience. She has an extensive background in technology development and healthcare, having worked for organizations such as Microsoft and Seattle Children’s Hospital. She also runs her own consulting business. In the PMP, Massoudi is focusing on machine learning and deep learning and anticipates graduating at the end of the 2027 spring quarter.
Devika Nair is a full-time PMP student, who took a break from an established career as a firmware engineer for SanDisk to attend the program. She has in-depth experience working with embedded systems. In the PMP, she is focused on broadening her skillset and integrating her knowledge of real-time embedded systems with machine learning. She also plans to graduate at the end of the 2027 spring quarter.
Massoudi and Nair worked together on their final project in the course, RespiraEdge, a real-time edge AI system for multimodal respiratory distress monitoring.
Why were you interested in this course?
Massoudi: My background is in neuroscience, and I also spent several years in technology development and consulting. I’m especially interested in how AI can be applied to healthcare. But I realized a couple of years ago that there was a gap in my knowledge, especially with how quickly AI and machine learning were developing. That’s what brought me back to school.
I initially was in the Applied Cybersecurity Engineering Certificate Program, and my first course was in TinyML. Once I took that course, I realized that the subject was really interesting to me, and so I switched to the master’s degree program.
The TinyML course was highly hands-on. We learned how to build a complete machine learning system for very small, resource-constrained devices. And for the final project, I built an object detection system using a camera. So, when I saw this course, I was interested because it approached embedded AI from a broader deep learning and real-time system perspective. We went deeper into different neural network architectures, and also into questions of efficiency, such as quantization and latency, memory, and how deep computation can be optimized for different types of hardware.
So, for me, the two courses were complementary. I was able to build on what I had learned in the TinyML course and apply it to very different and more complex applications, and specifically to our final project, RespiraEdge.
"I’d like to combine my background in neuroscience with what I’ve been learning in AI and engineering and work on problems where this technology can have a meaningful, real-world impact." — Bita Massoudi, PMP student
Nair: I was interested in this course because it was one where I felt I could connect my existing background in embedded systems with something I hadn’t worked with as deeply, which was machine learning. One of the things I appreciated most about the course was its hands-on nature. We had labs where we could implement and experiment with concepts, we could write reports, and we were able to see how things behaved, rather than just learning theory.
I would say one of the best things about the course I really want to highlight is that we often heard from guest speakers. We also got the opportunity to visit Wyze, so we could see how some of the ideas we discussed in the course were being applied beyond the classroom.
So, for me, it was the combination of machine learning, embedded systems, and the hands-on practical aspect that really made this course stand out.
What was it like to learn from Professor Shi?
Massoudi: I really enjoyed Professor Shi’s teaching style. He’s hands-on. He would teach the concepts in class, but then we had to apply them and build something that worked. So, it wasn’t just theoretical. He often brought in guest speakers, and he gave us opportunities for site visits, which I really liked because it connected what we were learning in the classroom with how these technologies are being used in industry. That practical side of the course was valuable to me.
Nair: I think the best part of Professor Shi’s course was his energy and enthusiasm throughout the lecture. What really stood out to me is how he emphasized that we needed to get practical experience, and he made sure the course was hands-on. We weren’t just learning concepts from PowerPoints or lectures, we were implementing these concepts through all of the assignments, which were labs. I think Professor Shi provided a very good balance between understanding the fundamentals and actually getting to experiment and build things. And I think that is what made the course so engaging.
Tell me about your final project, RespiraEdge.
[caption id="attachment_42289" align="alignright" width="560"] A closeup view of the RespiraEdge interactive dashboard, which presents information in an easy-to-understand, visual format.[/caption]
Massoudi: RespiraEdge started with a very simple question: Can we listen to someone’s breathing and use AI to recognize when their breathing pattern becomes abnormal? With my background in neuroscience and interest in healthcare, I was drawn to the idea of whether something as basic as breathing could provide useful information about a person’s condition.
The whole idea is that changes in breathing could be recognized early, and perhaps an AI system could help bring attention to these changes sooner. So, we developed RespiraEdge as a real-time respiratory monitoring system. It listens to breathing sounds and uses deep learning models to determine whether the respiratory pattern appears normal or abnormal.
As the project developed, we wanted the system to look at more than just one prediction, “normal or abnormal.” We added continuous monitoring, heart-rate information from a Bluetooth sensor, and a rule-based intelligent agent that combines respiratory classifications, heart rate, prediction confidence, and trends over time to indicate whether the person appears stable, should continue to be monitored, or may need further attention. We also developed an interactive dashboard that runs locally, so all that information can be presented in an easy-to-understand, visual format.
One of the things that I really liked about this whole system is that everything is processed locally. It doesn’t need to continuously send someone’s respiratory data, that personal information, to the cloud. And the prototype uses relatively inexpensive consumer hardware. It used my computer’s microphone and a heart rate monitor that cost roughly $20.
So, what started as a normal versus abnormal breathing classifier evolved into a complete, real-time monitoring system, which was really cool to see.
"RespiraEdge brought everything together for me. I started out thinking primarily about neural networks, and by the end of the project, I was thinking about the entire system, from the microphone, all the way to what the user sees on the dashboard."
— Devika Nair, PMP student
Nair: RespiraEdge is a device that can continuously listen to a person’s breathing through a microphone, and it can monitor their heart rate. The breathing audio is processed locally and transformed into data that our machine learning model can analyze.
In our case, we used an AI model that classified the respiratory activity as normal or abnormal. We then added the ability to stream the user’s heart rate over Bluetooth. So, we are not relying only on the breathing sound. The system combined those signals, and it displayed a risk state on a live dashboard. So, the main idea of our device was that everything happens locally. We don’t need to send the person’s audio to the cloud.
What I found particularly interesting about this project was that we weren’t just building a classifier, we were trying to make the device behave like a real, continuously running monitoring system. So, we had to deal with things like background noise, false alarms, and we had to make sure the system didn’t constantly switch between states.
Overall, what did you learn from the course?
[caption id="attachment_42291" align="alignright" width="560"] PMP student Devika Nair explains how RespiraEdge can incorporate heart-rate information from a Bluetooth sensor.[/caption]
Massoudi: The biggest thing I learned was that developing a good AI model is only part of building a real-time intelligent system. You also have to think about computational efficiency, latency, memory, and the hardware. And then when you deploy the device, you have to think about sensors, data, noise, confidence, and reliability through the entire pipeline.
RespiraEdge brought everything together for me. I started out thinking primarily about neural networks, and by the end of the project, I was thinking about the entire system, from the microphone, all the way to what the user sees on the dashboard.
Nair: For me, this course was about the integration of machine learning and embedded systems. Coming from a firmware and embedded systems background, I was already used to thinking about reliability, real-time behavior, and edge cases. But I think this course really helped me see how important those same considerations are when you are building a machine learning system.
Throughout the labs, and especially when working on RespiraEdge, I learned that you can have a model that performs well on your test data, but once you put it into a real environment, you have to think about so many factors, such as noise, data differences, false alarms, and how the system behaves continuously. So, the course changed my perspective from, “Does the model work?” to “Does the whole system in its entirety work reliably?”
How will you use this knowledge going forward?
Massoudi: Going forward, I’d like to continue working at the intersection of AI, engineering, and healthcare. With a background in neuroscience, coming back to school gave me an opportunity to build the machine learning and engineering side of my knowledge. This course, in particular, added another piece to that by teaching me to think beyond just the AI model and more about how you build and deploy a real-time, intelligent system. I’d like to combine my background in neuroscience with what I’ve been learning in AI and engineering and work on problems where this technology can have a meaningful, real-world impact.
Nair: For me, because my background has always been in the same domain, embedded systems, this course has given me confidence and hands-on experience with machine learning as well. I now have a better understanding of how a machine learning model can fit into an embedded real-time system.
In my career, I see myself using these skills, especially at the intersection of embedded systems, firmware, and intelligent systems. So, even if I am not personally developing the machine learning model myself, understanding how the model works, what data it needs, and what it takes to deploy in a real system will help me work more effectively with teams who develop machine learning and AI systems.
Jayce Gaddis
[caption id="attachment_42293" align="alignright" width="560"] PMP student Jayce Gaddis demonstrates Dog SAURON, a smart, computer-vision-guided laser toy, created to keep his high-energy dog engaged and active.[/caption]
Jayce Gaddis is a part-time PMP student who works at Amazon as a system development engineer. He is part of Amazon Leo, formerly known as Project Kuiper, the company’s low-Earth-orbit satellite internet initiative designed to provide broadband connectivity to underserved communities around the world.
Gaddis specializes in signal processing, analog/mixed-signal hardware design, register-transfer level (RTL) development, and firmware development. He says he is most passionate about developing cutting-edge technologies that make a difference globally. He anticipates graduating at the end of the 2028 spring quarter.
His final course project was a smart, computer-vision-guided laser toy, which he created to keep his high-energy dog engaged and active. He named the project, “Dog SAURON (Smart Autonomous Unit for Redirecting Overstimulated Nonsense),” a tongue-in-cheek title that reflects both the toy’s purpose and outer shell design, which is built to look like the tower of Barad-dûr in Tolkien's classic Lord of the Rings books.
Why were you interested in this course?
Gaddis: Honestly, it sounded really fun. I didn’t know a lot about machine learning or any of these processing algorithms at the time, so it piqued my interest. I saw that it would be an open-ended course where I could work on fun projects that were exciting to me.
What was it like to learn from Professor Shi?
Gaddis: He was a very helpful professor. He had really good insights and focused a lot more on industry, which was also good. It was a different change of pace from other courses that I’ve taken in the program. For example, he brought in some engineers from Wyze to speak a few times because they were applying the same things in their work that we were learning in class. They were able to teach us how they were applying the concepts we were learning, and that was really good.
Tell me about your final project, Dog SAURON.
[caption id="attachment_42295" align="alignright" width="560"] A photo illustration showing Gaddis's vision for what Dog SAURON could look like after the plastic toy surround is built and a few more features are added. Image provided by Jayce Gaddis.[/caption]
Gaddis: Sure, Dog SAURON is a smart, computer-vision-guided laser toy that I made to keep my dog entertained. I have a Border Collie lab mix, so he has all of the energy and all of the silliness that breed is known for. I had found a laser toy for him on Amazon, and it randomly points a laser around the room for a pet to chase. However, when you have a really smart dog, like a Border Collie, they pick up on the pattern and get bored. So, I got inspired to make something a little bit smarter.
In this course, we were learning about how to use machine learning to find where things are in a frame. And we were also learning about how to classify things, for example, how to tell where the floor is and where the walls are. I saw it as the perfect application to keep my dog engaged. It was a really fun project to work on too.
So, I started developing, building my system on top of the laser toy. I used an Arduino fitted with a camera and shrunk the machine learning model to fit on this little Arduino. That was the most challenging part of the whole project. I tried to change and re-solder the part, but it was cheaply made, and it broke. I really need to make my own circuit board before the device will work 100 percent, but all the necessary algorithms are there.
And then, to move the laser pointer, I used a lot of what I learned from a previous course in control systems. I was able to make a control system loop to guide the laser pointer. I also trained the model to only shine the laser on the floor (rather than on the walls or elsewhere) as a safety feature.
Overall, what did you learn from the course?
Gaddis: I went into this course with only a cursory understanding of AI. I understood the math behind it, at a high level. But I did not know any of the fundamentals that we were taught in this course. Basically, I went in almost blind.
I ended up learning how to train and set up the AI and machine learning models. I also learned that there are different types of transformers to use in different circumstances, and some work better than others. There’s always tradeoffs in this work, so I learned all of that.
"In this course, we were learning about how to use machine learning to find where things are in a frame. And we were also learning about how to classify things, for example, how to tell where the floor is and where the walls are. I saw it as the perfect application to keep my dog engaged. It was a really fun project to work on too." — Jayce Gaddis, PMP student
I also learned how to shrink an AI model down to fit into an embedded system. It’s one thing to run the model on your laptop computer, and a lot of the students were able to do that. It’s a whole other thing to try to shrink that AI model into a smaller, embedded system.
How will you use this knowledge going forward?
[caption id="attachment_42297" align="alignright" width="560"] A closeup of the microcontroller inside of Dog SAURON. In the background, the furry, high-energy inspiration for this project.[/caption]
Gaddis: In my current job, I don’t do a lot of programming, so I couldn’t see using this knowledge there. But I don’t plan to be in this role forever, and the course gave me a good understanding of other systems and other embedded technologies that I have to work with as a systems engineer, so it built up my strength in that area.
Also, I have to work with the embedded firmware and hardware teams in my current role. This course gave me a better understanding of what is happening when those teams are running these AI models. For example, with the array we are working on, you could use AI to figure out where to steer the antenna beam. So, this course provided me with enough understanding that I could work with them on that, provision appropriate resources, and provide them with the right type of processor for that purpose.
From a larger perspective, the Professional Master’s Program started contributing to my professional goals from the beginning. I moved into the role I have now right after starting in the PMP, and it helped me to adapt really quickly. For example, I took a course in the fundamentals of Maxwell’s equations, and after every class, without fail, I was applying it to my work the next day. I’d be debugging an antenna or an array, and I was able to apply what I learned the day before about reflections, impedance matching, and everything else. The PMP is also flexible enough to allow me to keep working and supporting my family while I’m going to school at night. The program has just been awesome in these ways.
Learn more about the Deep Learning for Embedded Real Time Intelligence course and the UW ECE Professional Master’s Program on the UW ECE website.
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[post_content] => Article by Wayne Gillam / Photos by Ryan Hoover
[caption id="attachment_42273" align="alignright" width="660"] Students in the UW ECE Professional Master’s Program, including (from left to right) Bita Massoudi, Jayce Gaddis, and Devika Nair, are learning how to embed advanced AI capabilities into resource-constrained electronics. In the PMP course Deep Learning for Embedded Real Time Intelligence they are taught how to build and deploy AI models that use deep learning in Internet of Things and mobile edge devices.[/caption]
Most Americans today live their lives immersed in technology. Mobile devices, such as smartphones, tablets, and laptops, are common platforms for working, playing, and staying connected with the world around us. Smart televisions, thermostats, and home appliances enabled by the Internet of Things (IoT) make life at home more comfortable and entertaining. And wearable technologies, such as smart watches and rings, help keep us informed about our health and well-being.
Many of these devices rely on machine learning and artificial intelligence (AI) to perform tasks such as recognizing images, understanding speech, and analyzing large amounts of data. And increasingly, these capabilities are powered by deep learning, a subset of machine learning that uses multilayered neural networks to identify complex patterns in data.
This creates a challenge for engineers. As AI becomes ever more integrated into everyday devices, there is a need to develop systems that incorporate deep learning but can still operate efficiently at the network edge, processing data locally on devices that often have limited computing resources, memory, power, and internet connectivity.
Now, students in UW ECE’s Professional Master’s Program are learning how to embed these advanced AI capabilities into resource-constrained electronics. In the course, Deep Learning for Embedded Real Time Intelligence, they learn how to build and deploy AI models that use deep learning in IoT and mobile edge devices.
This course is offered annually during spring quarter and receives generous support from industry partners. For example, during the 2026 spring quarter, students received a starter kit for their final projects from Wyze, a consumer technology company known for developing smart home devices and wireless cameras. Wyze also sent engineers to speak with students about how concepts taught in the course are applied in commercial products.
[caption id="attachment_42275" align="alignleft" width="250"] UW ECE Professor Richard Shi[/caption]
The course is taught by UW ECE Professor Richard Shi, whose research and teaching focus on embedded systems, AI, machine learning, and hardware-software co-design.
“As AI becomes increasingly embedded in everyday technology, engineers need to understand both machine learning algorithms and the hardware systems that run them,” Shi said. “This course gives students hands-on experience designing, optimizing, and deploying AI solutions for real-world embedded devices, helping them develop skills that are highly relevant across today’s technology industry.”
In addition to being offered through the PMP curriculum, this course is also part of the PMP Certificate Program in Machine Learning and Deep Learning, which provides students with a deep dive into the field.
A common theme echoed throughout the course is that successful AI applications require far more than accurate models. Students must also consider hardware limitations, continuous performance, reliability, and user experience. So, through hands-on labs, industry engagement, and ambitious final projects, students in this course gain practical experience deploying AI in real-world embedded environments.
Three students who completed the course during the 2026 spring quarter shared their experiences and key takeaways:
Bita Massoudi and Devika Nair
[caption id="attachment_42284" align="alignright" width="560"] PMP student Bita Massoudi demonstrates RespiraEdge, a real-time edge AI system for multimodal respiratory distress monitoring that can run on a laptop.[/caption]
Bita Massoudi is a part-time PMP student who holds a doctoral degree in neuroscience. She has an extensive background in technology development and healthcare, having worked for organizations such as Microsoft and Seattle Children’s Hospital. She also runs her own consulting business. In the PMP, Massoudi is focusing on machine learning and deep learning and anticipates graduating at the end of the 2027 spring quarter.
Devika Nair is a full-time PMP student, who took a break from an established career as a firmware engineer for SanDisk to attend the program. She has in-depth experience working with embedded systems. In the PMP, she is focused on broadening her skillset and integrating her knowledge of real-time embedded systems with machine learning. She also plans to graduate at the end of the 2027 spring quarter.
Massoudi and Nair worked together on their final project in the course, RespiraEdge, a real-time edge AI system for multimodal respiratory distress monitoring.
Why were you interested in this course?
Massoudi: My background is in neuroscience, and I also spent several years in technology development and consulting. I’m especially interested in how AI can be applied to healthcare. But I realized a couple of years ago that there was a gap in my knowledge, especially with how quickly AI and machine learning were developing. That’s what brought me back to school.
I initially was in the Applied Cybersecurity Engineering Certificate Program, and my first course was in TinyML. Once I took that course, I realized that the subject was really interesting to me, and so I switched to the master’s degree program.
The TinyML course was highly hands-on. We learned how to build a complete machine learning system for very small, resource-constrained devices. And for the final project, I built an object detection system using a camera. So, when I saw this course, I was interested because it approached embedded AI from a broader deep learning and real-time system perspective. We went deeper into different neural network architectures, and also into questions of efficiency, such as quantization and latency, memory, and how deep computation can be optimized for different types of hardware.
So, for me, the two courses were complementary. I was able to build on what I had learned in the TinyML course and apply it to very different and more complex applications, and specifically to our final project, RespiraEdge.
"I’d like to combine my background in neuroscience with what I’ve been learning in AI and engineering and work on problems where this technology can have a meaningful, real-world impact." — Bita Massoudi, PMP student
Nair: I was interested in this course because it was one where I felt I could connect my existing background in embedded systems with something I hadn’t worked with as deeply, which was machine learning. One of the things I appreciated most about the course was its hands-on nature. We had labs where we could implement and experiment with concepts, we could write reports, and we were able to see how things behaved, rather than just learning theory.
I would say one of the best things about the course I really want to highlight is that we often heard from guest speakers. We also got the opportunity to visit Wyze, so we could see how some of the ideas we discussed in the course were being applied beyond the classroom.
So, for me, it was the combination of machine learning, embedded systems, and the hands-on practical aspect that really made this course stand out.
What was it like to learn from Professor Shi?
Massoudi: I really enjoyed Professor Shi’s teaching style. He’s hands-on. He would teach the concepts in class, but then we had to apply them and build something that worked. So, it wasn’t just theoretical. He often brought in guest speakers, and he gave us opportunities for site visits, which I really liked because it connected what we were learning in the classroom with how these technologies are being used in industry. That practical side of the course was valuable to me.
Nair: I think the best part of Professor Shi’s course was his energy and enthusiasm throughout the lecture. What really stood out to me is how he emphasized that we needed to get practical experience, and he made sure the course was hands-on. We weren’t just learning concepts from PowerPoints or lectures, we were implementing these concepts through all of the assignments, which were labs. I think Professor Shi provided a very good balance between understanding the fundamentals and actually getting to experiment and build things. And I think that is what made the course so engaging.
Tell me about your final project, RespiraEdge.
[caption id="attachment_42289" align="alignright" width="560"] A closeup view of the RespiraEdge interactive dashboard, which presents information in an easy-to-understand, visual format.[/caption]
Massoudi: RespiraEdge started with a very simple question: Can we listen to someone’s breathing and use AI to recognize when their breathing pattern becomes abnormal? With my background in neuroscience and interest in healthcare, I was drawn to the idea of whether something as basic as breathing could provide useful information about a person’s condition.
The whole idea is that changes in breathing could be recognized early, and perhaps an AI system could help bring attention to these changes sooner. So, we developed RespiraEdge as a real-time respiratory monitoring system. It listens to breathing sounds and uses deep learning models to determine whether the respiratory pattern appears normal or abnormal.
As the project developed, we wanted the system to look at more than just one prediction, “normal or abnormal.” We added continuous monitoring, heart-rate information from a Bluetooth sensor, and a rule-based intelligent agent that combines respiratory classifications, heart rate, prediction confidence, and trends over time to indicate whether the person appears stable, should continue to be monitored, or may need further attention. We also developed an interactive dashboard that runs locally, so all that information can be presented in an easy-to-understand, visual format.
One of the things that I really liked about this whole system is that everything is processed locally. It doesn’t need to continuously send someone’s respiratory data, that personal information, to the cloud. And the prototype uses relatively inexpensive consumer hardware. It used my computer’s microphone and a heart rate monitor that cost roughly $20.
So, what started as a normal versus abnormal breathing classifier evolved into a complete, real-time monitoring system, which was really cool to see.
"RespiraEdge brought everything together for me. I started out thinking primarily about neural networks, and by the end of the project, I was thinking about the entire system, from the microphone, all the way to what the user sees on the dashboard."
— Devika Nair, PMP student
Nair: RespiraEdge is a device that can continuously listen to a person’s breathing through a microphone, and it can monitor their heart rate. The breathing audio is processed locally and transformed into data that our machine learning model can analyze.
In our case, we used an AI model that classified the respiratory activity as normal or abnormal. We then added the ability to stream the user’s heart rate over Bluetooth. So, we are not relying only on the breathing sound. The system combined those signals, and it displayed a risk state on a live dashboard. So, the main idea of our device was that everything happens locally. We don’t need to send the person’s audio to the cloud.
What I found particularly interesting about this project was that we weren’t just building a classifier, we were trying to make the device behave like a real, continuously running monitoring system. So, we had to deal with things like background noise, false alarms, and we had to make sure the system didn’t constantly switch between states.
Overall, what did you learn from the course?
[caption id="attachment_42291" align="alignright" width="560"] PMP student Devika Nair explains how RespiraEdge can incorporate heart-rate information from a Bluetooth sensor.[/caption]
Massoudi: The biggest thing I learned was that developing a good AI model is only part of building a real-time intelligent system. You also have to think about computational efficiency, latency, memory, and the hardware. And then when you deploy the device, you have to think about sensors, data, noise, confidence, and reliability through the entire pipeline.
RespiraEdge brought everything together for me. I started out thinking primarily about neural networks, and by the end of the project, I was thinking about the entire system, from the microphone, all the way to what the user sees on the dashboard.
Nair: For me, this course was about the integration of machine learning and embedded systems. Coming from a firmware and embedded systems background, I was already used to thinking about reliability, real-time behavior, and edge cases. But I think this course really helped me see how important those same considerations are when you are building a machine learning system.
Throughout the labs, and especially when working on RespiraEdge, I learned that you can have a model that performs well on your test data, but once you put it into a real environment, you have to think about so many factors, such as noise, data differences, false alarms, and how the system behaves continuously. So, the course changed my perspective from, “Does the model work?” to “Does the whole system in its entirety work reliably?”
How will you use this knowledge going forward?
Massoudi: Going forward, I’d like to continue working at the intersection of AI, engineering, and healthcare. With a background in neuroscience, coming back to school gave me an opportunity to build the machine learning and engineering side of my knowledge. This course, in particular, added another piece to that by teaching me to think beyond just the AI model and more about how you build and deploy a real-time, intelligent system. I’d like to combine my background in neuroscience with what I’ve been learning in AI and engineering and work on problems where this technology can have a meaningful, real-world impact.
Nair: For me, because my background has always been in the same domain, embedded systems, this course has given me confidence and hands-on experience with machine learning as well. I now have a better understanding of how a machine learning model can fit into an embedded real-time system.
In my career, I see myself using these skills, especially at the intersection of embedded systems, firmware, and intelligent systems. So, even if I am not personally developing the machine learning model myself, understanding how the model works, what data it needs, and what it takes to deploy in a real system will help me work more effectively with teams who develop machine learning and AI systems.
Jayce Gaddis
[caption id="attachment_42293" align="alignright" width="560"] PMP student Jayce Gaddis demonstrates Dog SAURON, a smart, computer-vision-guided laser toy, created to keep his high-energy dog engaged and active.[/caption]
Jayce Gaddis is a part-time PMP student who works at Amazon as a system development engineer. He is part of Amazon Leo, formerly known as Project Kuiper, the company’s low-Earth-orbit satellite internet initiative designed to provide broadband connectivity to underserved communities around the world.
Gaddis specializes in signal processing, analog/mixed-signal hardware design, register-transfer level (RTL) development, and firmware development. He says he is most passionate about developing cutting-edge technologies that make a difference globally. He anticipates graduating at the end of the 2028 spring quarter.
His final course project was a smart, computer-vision-guided laser toy, which he created to keep his high-energy dog engaged and active. He named the project, “Dog SAURON (Smart Autonomous Unit for Redirecting Overstimulated Nonsense),” a tongue-in-cheek title that reflects both the toy’s purpose and outer shell design, which is built to look like the tower of Barad-dûr in Tolkien's classic Lord of the Rings books.
Why were you interested in this course?
Gaddis: Honestly, it sounded really fun. I didn’t know a lot about machine learning or any of these processing algorithms at the time, so it piqued my interest. I saw that it would be an open-ended course where I could work on fun projects that were exciting to me.
What was it like to learn from Professor Shi?
Gaddis: He was a very helpful professor. He had really good insights and focused a lot more on industry, which was also good. It was a different change of pace from other courses that I’ve taken in the program. For example, he brought in some engineers from Wyze to speak a few times because they were applying the same things in their work that we were learning in class. They were able to teach us how they were applying the concepts we were learning, and that was really good.
Tell me about your final project, Dog SAURON.
[caption id="attachment_42295" align="alignright" width="560"] A photo illustration showing Gaddis's vision for what Dog SAURON could look like after the plastic toy surround is built and a few more features are added. Image provided by Jayce Gaddis.[/caption]
Gaddis: Sure, Dog SAURON is a smart, computer-vision-guided laser toy that I made to keep my dog entertained. I have a Border Collie lab mix, so he has all of the energy and all of the silliness that breed is known for. I had found a laser toy for him on Amazon, and it randomly points a laser around the room for a pet to chase. However, when you have a really smart dog, like a Border Collie, they pick up on the pattern and get bored. So, I got inspired to make something a little bit smarter.
In this course, we were learning about how to use machine learning to find where things are in a frame. And we were also learning about how to classify things, for example, how to tell where the floor is and where the walls are. I saw it as the perfect application to keep my dog engaged. It was a really fun project to work on too.
So, I started developing, building my system on top of the laser toy. I used an Arduino fitted with a camera and shrunk the machine learning model to fit on this little Arduino. That was the most challenging part of the whole project. I tried to change and re-solder the part, but it was cheaply made, and it broke. I really need to make my own circuit board before the device will work 100 percent, but all the necessary algorithms are there.
And then, to move the laser pointer, I used a lot of what I learned from a previous course in control systems. I was able to make a control system loop to guide the laser pointer. I also trained the model to only shine the laser on the floor (rather than on the walls or elsewhere) as a safety feature.
Overall, what did you learn from the course?
Gaddis: I went into this course with only a cursory understanding of AI. I understood the math behind it, at a high level. But I did not know any of the fundamentals that we were taught in this course. Basically, I went in almost blind.
I ended up learning how to train and set up the AI and machine learning models. I also learned that there are different types of transformers to use in different circumstances, and some work better than others. There’s always tradeoffs in this work, so I learned all of that.
"In this course, we were learning about how to use machine learning to find where things are in a frame. And we were also learning about how to classify things, for example, how to tell where the floor is and where the walls are. I saw it as the perfect application to keep my dog engaged. It was a really fun project to work on too." — Jayce Gaddis, PMP student
I also learned how to shrink an AI model down to fit into an embedded system. It’s one thing to run the model on your laptop computer, and a lot of the students were able to do that. It’s a whole other thing to try to shrink that AI model into a smaller, embedded system.
How will you use this knowledge going forward?
[caption id="attachment_42297" align="alignright" width="560"] A closeup of the microcontroller inside of Dog SAURON. In the background, the furry, high-energy inspiration for this project.[/caption]
Gaddis: In my current job, I don’t do a lot of programming, so I couldn’t see using this knowledge there. But I don’t plan to be in this role forever, and the course gave me a good understanding of other systems and other embedded technologies that I have to work with as a systems engineer, so it built up my strength in that area.
Also, I have to work with the embedded firmware and hardware teams in my current role. This course gave me a better understanding of what is happening when those teams are running these AI models. For example, with the array we are working on, you could use AI to figure out where to steer the antenna beam. So, this course provided me with enough understanding that I could work with them on that, provision appropriate resources, and provide them with the right type of processor for that purpose.
From a larger perspective, the Professional Master’s Program started contributing to my professional goals from the beginning. I moved into the role I have now right after starting in the PMP, and it helped me to adapt really quickly. For example, I took a course in the fundamentals of Maxwell’s equations, and after every class, without fail, I was applying it to my work the next day. I’d be debugging an antenna or an array, and I was able to apply what I learned the day before about reflections, impedance matching, and everything else. The PMP is also flexible enough to allow me to keep working and supporting my family while I’m going to school at night. The program has just been awesome in these ways.
Learn more about the Deep Learning for Embedded Real Time Intelligence course and the UW ECE Professional Master’s Program on the UW ECE website.
[post_title] => Students build smarter devices with embedded AI
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[post_content] => Article by Wayne Gillam, Photos by Ryan Hoover / UW ECE News
[caption id="attachment_42113" align="alignright" width="625"] In new undergraduate capstone courses offered by UW ECE, students are working together in teams to learn how to design and build microchips for specific, real-world applications. Shown above, chips designed by student teams in the Department’s Integrated Systems Capstone course and VLSI Capstone course.[/caption]
Microchips, also known as semiconductors, integrated circuits, or simply “chips” in engineering circles, are a marvel. These tiny chips may be small, but they punch far above their weight, functioning as the intelligent core of modern technology. Microchips use billions of microscopic transistors with nanometer dimensions to process information, sense the world around us, execute software instructions, provide a platform for communication, and store data. They power a vast array of technologies, ranging from the smartphone in your hand to artificial intelligence to global communication networks for airplanes and satellites.
During the COVID-19 pandemic, when supply chains around the world tightened, it became clear to the United States government how important microchips are to the economy and national security. This realization launched an ongoing effort to revitalize semiconductor design and manufacturing across the country. A crucial part of this initiative is workforce development — training the next generation of engineers who can design and build microchips.
UW ECE is helping to meet this important need in several different ways, including participating in programs aimed at semiconductor workforce development, such as UPWARDS for the Future, and by providing virtual reality training for students.
“I would like to emphasize that we are providing pathways for a student to graduate with a bachelor’s degree and pursue a viable career in integrated circuit design. Traditionally, this option was only available to those with master’s and doctoral degrees.”
— UW ECE Professor Chris Rudell
Now, in new undergraduate capstone courses taught by UW ECE professors Chris Rudell and Ang Li, students are working together in teams to learn how to design and build microchips for specific applications. In this rigorous curriculum, students not only design chips, but over the span of about a year, they have the opportunity to send their designs out for fabrication at an industrial foundry and follow up with testing to ensure their chips work in real-world settings.
“It is clear that the demand for chip designers has risen, particularly in the United States. This is a critical technology for defense applications, economic development, and high tech,” Rudell said. “Nationwide, we’re just not graduating enough students that have skills to do integrated circuit design. That’s the critical need our courses are helping to address.”
“These courses provide a realistic and comprehensive experience for students interested in the semiconductor design industry,” Li added. “We teach students valuable design skills as well as system understanding, testing, and post-silicon practices.”
A collaborative environment provides uncommon opportunities
[caption id="attachment_42115" align="alignright" width="525"] A closeup view of a chip designed by UW ECE doctoral student Elpida Karapepera and her student teammates in the 2025 spring quarter Integrated Systems Capstone course. This receiver chip significantly improves signal linearity and filters out strong interference. It is designed for advanced wireless communication applications, including Wi-Fi, Bluetooth, and software-defined radios. The chip design was described in a paper published at the 2026 IEEE International NEWCAS Conference. Karapepera, who now is a teaching assistant for the capstone course, received a Best Student Paper Award at the Conference for this work.[/caption]
These courses got their start at UW ECE in 2025 and receive support from the Center for Education of Microchip Designers, or CEMiD, which is based at the University of California, Los Angeles (UCLA). CEMiD, led by professor Behzad Razavi at UCLA, works with other universities across the nation to train students to become microchip designers. Further support for Rudell and Li’s courses is provided by Apple, Advanced Micro Devices (AMD), Muse Semiconductor, and Taiwan Semiconductor Manufacturing Company (TSMC), the world’s largest dedicated semiconductor foundry.
As one of eight universities participating in CEMiD’s pilot program in 2025, UW ECE stands out as a pioneer in developing comprehensive chip tapeout courses like these for undergraduate and graduate students. The term “tapeout” comes from the early days of semiconductor design and fabrication, when the data file containing every detail of a chip’s physical definition was loaded onto a magnetic reel and shipped in the fastest way possible to a chip fabrication facility. Today, this same file that signifies the completed chip design is transferred over the internet. The tapeout is the final stage of the chip design process, when a completed and verified digital design is sent to a foundry to be manufactured into a silicon chip.
For undergraduate students at UW ECE, there are two main avenues for studying chip design. One is the Integrated Systems Pathway, which provides a sequence of courses that culminate in the Integrated Systems Capstone, the analog, mixed-signal, and radio frequency (RF) chip tapeout course taught by Rudell and other professors at UW ECE, depending on the academic year. The other is the Very Large-Scale Integrated (VLSI) / Digital Systems Design Pathway, which puts students on track to take the VLSI Capstone, the digital chip tapeout course taught by Li. The capstone courses are cross-listed, so graduate students and those from other UW schools and departments can also have this educational opportunity.
[caption id="attachment_42117" align="alignleft" width="525"] UW ECE Professor Chris Rudell (right) instructing students in the Integrated Systems Capstone course. This undergraduate capstone course teaches students how to design analog, mixed-signal, and radio frequency (RF) chips to an industry-level standard.[/caption]
“As an undergraduate, there aren’t many opportunities to tape out a chip, let alone get the experience of designing a chip from start to finish,” said UW ECE alumnus Bryan Kim (BSECE ‘26), who took Rudell’s chip tapeout course during his senior year. “On top of that, by this point in the Pathway, the students who have stuck with it tend to be more motivated, which makes the group work more enjoyable.”
Students in both capstone courses can follow their designs through fabrication, which happens in the summer, and continues with testing and verification during the following academic year. Students who choose this option participate in a year-long experience that culminates in the design, fabrication, and testing of a functional microchip built for a specific application.
“I would like to emphasize that we are providing pathways for a student to graduate with a bachelor’s degree and pursue a viable career in integrated circuit design,” Rudell said. “Traditionally, this option was only available to those with master’s and doctoral degrees. The barrier to entry for a career in microchip design is quite high, but these courses shatter that obstacle for students by providing a real-world design experience.”
Advanced chip design, with real-world applications
[caption id="attachment_42119" align="alignright" width="525"] A closeup view of a chip designed by students in the VLSI Capstone course. This chip, titled “Bluetiful” by the student team that designed it, is a digital-only Bluetooth Low-Energy, or BLE, transmitter chip based on patented intellectual property from the lab of UW ECE Professor Matt Reynolds. The chip design was described in a paper published at IEEE RFID 2026, demonstrating UW ECE students working at a high level.[/caption]
The learning experience extends well beyond the classroom. In the tapeout courses, student teams are encouraged to turn their own ideas into microchip designs, and they are provided with a robust set of resources to do so. In addition to guidance from the instructors and teaching assistants, engineers from Apple and AMD meet regularly with students to evaluate chip designs and offer feedback. Students are also offered the chance to collaborate with UW faculty members, developing microchips for research projects.
Students in the Integrated Systems Pathway have been advised by UW professors Robert Miyaoki, Hossein Naghavi, Sajjad Moazeni, and industry collaborators at Boeing, Amazon, and Texas Instruments. Students in the VLSI Digital Systems Design Pathway have also been advised by UW ECE professors.
“Last year, there were two student teams working with professor Matt Reynolds on Bluetooth transmitters,” Li said. “This year, we have two teams working with professor Yiyue Luo, building an AI accelerator for her intelligent textile glove. And we have yet another team collaborating with professor Lih-Yuan Lin on using micro-LED [light-emitting diodes] to do data transmission.”
[caption id="attachment_42121" align="alignleft" width="525"] UW ECE Assistant Professor Ang Li (standing, second from right) advises students in the VLSI Capstone course. This undergraduate capstone course teaches students how to design and build industry-standard digital microchips for modern technology.[/caption]
As a result of this intellectual freedom, strong academic-industry partnership, and highly collaborative environment, many of the students’ chip designs are quite advanced. Some projects have even progressed beyond the classroom and into peer-reviewed publications and presentations at leading engineering conferences, such as the 2026 Institute of Electrical and Electronics Engineers (IEEE) International New Circuits and Systems (NEWCAS) Conference, the 2026 IEEE International Conference on Radio Frequency Identification (RFID 2026), and the 2026 IEEE Radio Frequency Integrated Circuits (RFIC) Symposium.
“Even after the course ended in Spring 2025, we continued testing the chip when it returned from fabrication to the Department in Autumn 2025,” said UW ECE alumna Kate Tseng (BSECE ‘26), who took Li’s chip tapeout course during her junior year. “Since the chip was successful, we were able to write a manuscript during the winter quarter and publish our paper at IEEE RFID 2026.”
“I’m excited to share that our work was accepted by the 2026 IEEE RFIC Symposium, proving that projects in this course reach a true professional publication standard,” said Zhuoran Wu (MSEE ‘26), an alumnus and incoming doctoral student who took Rudell’s chip tapeout course while completing his master’s degree. He added, “The most important thing this course taught me is how to make sure a design actually works when it is fabricated in silicon, not just on a computer screen.”
Looking ahead
[caption id="attachment_42123" align="alignright" width="525"] Students in the undergraduate VLSI Capstone course testing a field-programmable gate array (FPGA) prototype of their chip design before sending it out for fabrication.[/caption]
Going forward, Rudell said that he anticipates these capstone courses will be taught by a core group of faculty in the Department who are experts in different aspects of chip design, rotating from year to year and offering students a range of expertise along with a variety of experiences. He and Li are also facilitating a passing down of knowledge between generations of students. For example, they ask students from prior years to provide a presentation on their course experience to students in the current year. And they are continually integrating feedback from students to improve course infrastructure.
Rudell and Li also said that they look forward to growing industry partnerships and support for these chip tapeout courses as they help to meet the demand for qualified microchip designers.
“We are producing competitive engineers at UW ECE,” Li said. “Only very few schools and universities offer this type of curriculum, and it’s not just because of the need for funding, it’s also because of the effort required. It takes more than a year to train a cohort of students, and we are doing that every year. The Department is dedicated to this continuous, non-stop effort because we all realize how important it is.”
“This course provides an excellent platform to learn about something that is so critical to economic development, national defense, and high-performance technology applications,” Rudell said. “For most companies, what they are seeking are college graduates that have been trained in this area and know how to leverage the most advanced CAD [Computer-Aided Design] tools to design a chip. That is exactly what we are providing.”
The future looks bright for those who want to build an academic foundation at the UW for a career in integrated circuit design. By the time they graduate, UW ECE students are doing far more than simply learning about semiconductor technology. They are designing, fabricating, and testing real microchips, gaining valuable experience that prepares them well for a career in the semiconductor industry.
The undergraduate Integrated Systems Capstone course is offered during winter and spring quarters at UW ECE, and the undergraduate VLSI Capstone course is offered in spring quarter. For more information, contact UW ECE professors Chris Rudell and Ang Li.
[post_title] => Training the next generation of microchip designers
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[post_content] => Article by Wayne Gillam, Photos by Ryan Hoover / UW ECE News
[caption id="attachment_42076" align="alignright" width="575"] UW ECE Assistant Professor Yiyue Luo is known for her work in intelligent, or “smart,” textiles. At UW ECE, she and her students are expanding this relatively new line of research into developing other forms of intelligent wearable technology.[/caption]
UW ECE Assistant Professor Yiyue Luo found her professional focus in a surprising way: by choosing to avoid things she didn’t like.
Today, Luo leads the Wearable Intelligence Lab at the UW, where she develops intelligent wearable technologies that can monitor health, improve rehabilitation, and provide robots with a more human-like sense of touch.
“It’s easy for me to realize what I don’t like,” Luo said. “I try not to do what I don’t like, and then that choice tends to move me toward something that is a much better fit.”
This approach to life is reflected in how she first became involved in electrical and computer engineering.
Luo said she was interested in science and engineering from a young age, but in high school, she wasn’t sure which field she wanted to pursue. Her father, a materials scientist, encouraged her to follow in his footsteps.
Taking her father’s advice, Luo enrolled in materials science and engineering at the University of Illinois Urbana-Champaign. As she progressed through the program, however, she discovered that her interests were shifting. Undergraduate research experiences exposed her to electronics design, and she became increasingly drawn toward electrical engineering.
“The sense of touch is a physically grounded modality that humans take for granted. Information from tactile sensing is extremely valuable for many applications, but collecting it remains challenging because the necessary hardware often does not exist. In my lab, we are creating this needed hardware in new form factors that can capture tactile information in a scalable, comprehensive, and continuous way.” — UW ECE Assistant Professor Yiyue Luo
She had already begun participating in materials science research as an undergraduate, but during her sophomore year she decided to change direction and join the Rogers Research Group. This research group was led by Professor John A. Rogers (now at Northwestern University), who was well-known for his interdisciplinary work in materials science, electronics, and bioengineering.
[caption id="attachment_42080" align="alignright" width="475"] UW ECE postdoctoral scholar and Entrepreneurial Fellow Sen Zhang (left) with Luo in the Wearable Intelligence Lab. Luo is trying on a MultiSensKnit prototype, a sensor-packed knitted sleeve she developed with Zhang that is designed for rehabilitation assessment.[/caption]
At the time, Rogers was exploring epidermal electronics, ultra-thin, flexible electronic devices that stick to human skin like a temporary tattoo. These devices can monitor physiological signals, serve as highly sensitive electronic interfaces, and even deliver targeted therapies. Luo said she found the research fascinating, and it helped to lay a foundation for her later work.
The experience convinced Luo that the future she wanted to pursue was at the intersection of electronics, computing, and human-centered applications.
After receiving her bachelor’s degree in materials science and engineering, Luo decided to pursue a doctoral degree in electrical engineering and computer science. The transition was unconventional because her undergraduate training was in materials science rather than electrical engineering. But for Luo, the decision felt like a natural progression toward the work that excited her most.
She was accepted into MIT’s EECS program, where she was advised by professors Wojciech Matusik and Tomás Palacios.
It was in Matusik’s Computational Design & Fabrication Group where Luo found her true calling. There, she was introduced to a digital knitting machine that transforms digital designs into knitted fabrics. Matusik and his students used the machine to knit conductive yarn into garments, creating a type of wearable technology known as “intelligent textiles” — fabrics embedded with sensors, actuators, and conductive fibers.
[caption id="attachment_42083" align="alignleft" width="475"] Luo adjusting the digital knitting machine in the Wearable Intelligence Lab. This device transforms digital designs into professional-quality, knitted fabrics.[/caption]
Intelligent textiles, also known as “smart textiles,” can sense, store, process, and communicate information seamlessly and unobtrusively. They have a wide range of potential applications, including monitoring physiological signals and acting as control interfaces for electronic devices.
As she began working with these textile-based sensing systems, Luo realized that fabric offered a uniquely powerful form factor because of its ubiquity. People wear clothing every day and constantly encounter fabrics through furniture, carpets, and bedding. She also recognized the potential these technologies had to capture tactile information that cameras, microphones, and conventional electronics cannot easily detect.
“With smart textiles, we can sense, monitor, and embed important, physically grounded information, well beyond what images, audio, and text can provide,” Luo said. “This information can be fundamental for understanding human behavior and activity, so I think a lot of opportunities emerge from that.”
While at MIT, Luo produced cutting-edge wearable textile technologies, including digitally embroidered smart gloves that can record, reproduce, and transfer tactile interactions. These gloves can help users learn complex physical hand movements and can enable touch-sensitive robot teleoperation. She also created AI-powered sensing textiles — including vests, socks, and gloves — that can record and monitor a person’s physical interactions with their environment in real time. The technology has a wide range of potential applications in healthcare and robotics. KnitUI, another system she developed, is a machine-knitted user interface that allows users to operate devices through pressure-sensitive fabric.
“I believe students need to experience things hands-on. That’s how I learned, especially as an undergraduate. I feel like I didn’t truly understand research until I actually touched the textile samples and gained a tangible understanding of how things worked. I think this is an important part of my research and my teaching philosophy.” — UW ECE Assistant Professor Yiyue Luo
Luo’s research has been published in leading interdisciplinary journals, such as Nature Electronics, and at top human-computer interaction and robotics conferences. Her work has also been featured in major media outlets and exhibitions.
Her contributions have earned numerous honors, including Best Paper at the 2025 ACM Conference on Human Factors in Computing Systems (CHI) and Best Paper Honorable Mentions at the 2025 ACM Symposium on User Interface Software and Technology (UIST) and CHI 2021. Additional recognitions include the 2025 Sony Faculty Innovation Award, the MIT EECS Jin Au Kong PhD Thesis Award, and selection to Forbes’ 2024 30 under 30 North America list in Science.
By the time she completed her doctorate, Luo had established herself as a rising researcher in wearable intelligence and smart textiles. The next step was building a research program of her own.
After receiving her doctoral degree from MIT, Luo joined UW ECE in September 2024 as an assistant professor.
“UW ECE is special in that it’s technical and rigorous, but it’s also open-minded,” Luo said. “It has faculty with deep fundamental knowledge about electronics, and it provides a supportive, interdisciplinary environment. This all allows me to collaborate on impactful projects that have potential for major contributions to science and technology.”
The Wearable Intelligence Lab
[caption id="attachment_42089" align="alignright" width="475"] Luo and her students use the digital knitting machine in her lab to knit together traditional yarns (colorful spools) with conductive yarn (gray spool in the foreground), creating fabric that is the basis for different types of intelligent wearable technology.[/caption]
At the UW, Luo established the Wearable Intelligence Lab to advance the integration of wearable technology and artificial intelligence into end-to-end systems for healthcare, robotics, and human-computer interaction.
Under Luo’s guidance, students in the lab are developing new approaches to tactile sensing and wearable intelligence. UW ECE doctoral student Devin Murphy has developed a smart glove with a sense of touch that could support rehabilitation medicine and help teach robots tactile sensing capabilities. UW ECE doctoral student Hongyu Mao and postdoctoral Entrepreneurial Fellow Sen Zhang are working with Luo to develop MultiSensKnit, a sensor-packed knitted sleeve designed for rehabilitation assessment. Another UW ECE doctoral student, Chankyu (Charlie) Han, has developed MagBall, a magnetic-ball sensor capable of capturing subtle interactions across glass, metal, and human skin. Other lab projects include a glove that teaches the user CPR, machine-knitted magnetoactive textiles that provide sensing and haptic feedback for various applications, and sensorimotor stickies, small patches that can deliver localized, on-body sensing and haptic cues in real time.
“The sense of touch is a physically grounded modality that humans take for granted. Information from tactile sensing is extremely valuable for many applications, but collecting it remains challenging because the necessary hardware often does not exist,” Luo said. “In my lab, we are creating this needed hardware in new form factors that can capture tactile information in a scalable, comprehensive, and continuous way.”
[caption id="attachment_42091" align="alignleft" width="475"] Luo demonstrates a digitally machine-knitted assistive glove, designed to support hand movement.[/caption]
Luo’s research is interdisciplinary by nature.
At the UW, she collaborates with faculty and students across multiple disciplines to explore new applications for wearable technology and tactile sensing. She is working with UW ECE Professor Chet Moritz, who holds joint appointments in UW Medicine, on rehabilitation technologies, including the MultiSensKnit sleeve she is developing with Zhang. She has also collaborated with UW ECE Associate Professor Sam Burden on equipping robots with human-like sensing capabilities for manipulation tasks and with Siddhartha (Sidd) Srinivasa, a professor in the Paul G. Allen School of Computer Science & Engineering, on human-computer interaction. Beyond engineering and computer science, Luo and UW ECE doctoral student Devin Murphy are brainstorming new tactile-sensing applications with UW Assistant Professor Meichun Liu and her industrial design students. Luo also provides digital knitting machine workshops in the Allen School’s Fabrication Research Lab, helping to introduce students from a variety of disciplines to wearable computing and textile-based technologies.
As an educator, Luo instructs and mentors a mix of undergraduate and graduate students. She said she enjoys teaching and learning from her students as well as watching them develop confidence in their independent thinking. To help facilitate that growth, she encourages students to remain open-minded and pursue experiential learning alongside their academic coursework.
“I believe students need to experience things hands-on,” Luo said. “That’s how I learned, especially as an undergraduate. I feel like I didn’t truly understand research until I actually touched the textile samples and gained a tangible understanding of how things worked. I think this is an important part of my research and my teaching philosophy.”
For more information about UW ECE Assistant Professor Yiyue Luo, visit her bio page and the Wearable Intelligence Lab website.
[post_title] => Yiyue Luo — developing intelligent wearable technology for healthcare, robotics and human-computer interaction
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[post_date] => 2026-08-28 15:41:10
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[post_content] => Adapted from an article by Kristin Osborne / Paul G. Allen School of Computer Science & Engineering
[caption id="attachment_41949" align="alignright" width="525"] UW ECE doctoral student Malek Itani has received a 2026 Marconi Society Paul Baran Young Scholar Award for exceptional early-career research that is advancing the future of information and communication technology.[/caption]
In 2022, UW ECE doctoral student Malek Itani purchased his first pair of Apple Airpods Pro in a pre-holiday sale. When he put them in his ears, something clicked — and it wasn’t a sound, but rather an idea.
“I put them on my ears, and I turned on noise canceling, and suddenly I felt like I was in my own personal space,” said Itani, a research assistant in the Mobile Intelligence Lab led by Allen School professor Shyam Gollakota. “I thought, ‘Wow, we can do something here.’
“But the model I was working on at the time was kind of huge, and not real-time, and definitely not something you can put on earbuds,” he continued. “And Shyam said, ‘But what if you can?’ “
Itani embraced the challenge. And after four years of steady and, at times, astounding progress, he received a Marconi Society Paul Baran Young Scholar Award for exceptional early-career research that is advancing the future of information and communications technology (ICT). Itani is one of only three scholars selected from a record-high number of nominees from around the world; he and his fellow honorees will be formally recognized at the Marconi Awards Gala & Institute Forums November 4-6 in San Francisco, California.
“Malek has been a key part of every major contribution to the field of superhuman hearing in recent years,” said Gollakota. “He entered his Ph.D. with a background in RF and backscatter, but he rapidly mastered audio signal processing and deep learning, which is very impressive.”
As an undergraduate, Itani was eager to explore different areas of his chosen field. He dabbled in the aforementioned radiofrequency (RF) communication, embedded systems, robotics and even competitive programming — all the while resisting well-meaning suggestions that he specialize. That breadth of experience was an asset in Gollakota’s lab, where the research is cross-disciplinary and the members approach problems from different, sometimes unexpected, angles.
Itani embodied this ethos during his first foray into the soundscape, which focused not on in-ear capabilities but around-the-room. In his first paper as a primary author, Itani and co-primary author Tuochao Chen, a Ph.D. student in the Allen School, introduced acoustic swarms, a system that creates speech zones in a room by tracking and separating multiple speakers simultaneously. The system consists of a neural network paired with a set of small robotic microphones that self-distribute across a space using only sound — no cameras or special substrate required. The robots automatically return to their charging station after deployment, making the system portable and easy to set up in new locations.
As it turned out, the project was Itani’s ideal introduction to his new line of research.
“The transition from RF to audio is actually simple, because you work with waves and frequencies — but instead of looking at gigahertz, you’re now looking at kilohertz,” Itani explained, “In some sense, it’s easier working with sound, because there’s less data to process. And it’s also more fun to work with, because you get to hear the end product.”
It was when he teamed up with another labmate, Bandhav Veluri (Ph.D., ‘25), on a project called Waveformer that he began to embrace this new direction.
“I had a lot of background in embedded systems because of my undergraduate work and because of the robots,” Itani said. “I was able to take that and port it over to an embedded system, run it in real time, and integrate it with the noise-cancelling headsets. That’s where I started to really learn about real-time audio processing.”
"I feel like I’ve hit a niche that’s going to be so transformational, and it’s going to be in billions of devices. It’s going to change the way we hear the world." — UW ECE doctoral student Malek Itani, 2026 Paul Baran Young Scholar
The result was the first neural network capable of real-time, streaming target sound extraction, which the researchers then translated into semantic hearing. Using off-the-shelf headphones paired with a smartphone, Itani and Veluri created a system that enabled the wearer to tailor what sounds they hear in their environment. For example, a person could program the device so that they could hear bird song while walking in the park but not the sound of nearby traffic. A subsequent project, target speech hearing, enabled wearers to focus on the voice of a single companion in a crowd simply by looking at them. The system leverages AI to learn and latch onto the target person’s speech patterns, which it plays back to the wearer in real time while canceling out other voices.
Itani and Chen then extended the wearer’s control over their soundscape from selected sounds to a selected space with a prototype headset that enabled the wearer to create a sound bubble. All sounds within the bubble’s perimeter are heard clearly; sounds outside the bubble are muffled or silenced. An onboard neural network determines which sounds to amplify or suppress based on the distance of each source from the embedded microphones.
That successful proof of concept inspired Itani to aim smaller and refine the technology for earbuds and hearing aids.
“Hearing aids are a natural use case,” Itani said. “In a noisy environment, hearing aids will amplify everything, but if you use AI you can amplify specific sounds that people care about. And you can recover not only what they would have heard, but you can also recover things that humans normally can’t hear. That’s where the concept of superhuman hearing comes from — you’re extending what’s possible with normal hearing.”
But this use case required the team to incorporate AI into devices with significant power and processing constraints. Last year, Itani, Chen and Gollakota partially answered that question with the development of TF-MLPNet, the first real-time neural speech separation network capable of running on low-power hearables like earbuds and hearing aids. They achieved another first with the introduction of NeuralAids, a programmable on-device AI platform for wireless hearables that achieves real-time speech enhancement under strict power constraints.
It wasn’t long before the team’s progress attracted the attention of industry. The team co-founded a UW startup, Hearvana AI, which raised $6 million last fall to support their push to bring acoustic intelligence to market.
As for what happens next, Itani says to stay tuned.
“I feel like I’ve hit a niche that’s going to be so transformational, and it’s going to be in billions of devices. It’s going to change the way we hear the world,” Itani said. ”Because of how important this is going to be, and how much this is going to change people’s lives, it genuinely feels like I have this responsibility to push this forward. I get to impact many, many people with this.”
To learn more, read the Marconi Society announcement and Itani’s Young Scholar profile, and visit Itani’s personal website.
[post_title] => UW ECE student Malek Itani earns Marconi Society Young Scholar Award for advancing ‘superhuman’ hearing with AI
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[post_date] => 2026-08-05 10:26:53
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[post_content] => By Wayne Gillam / UW ECE News
[caption id="attachment_41758" align="alignright" width="580"] UW ECE Assistant Professor Hossein Naghavi is leading one of 278 research projects chosen for the U.S. Department of Energy’s Genesis Mission from more than 5,000 applicants nationwide. His project is focused on developing an artificially intelligent augmented reality headset that would enable the user to see through smoke, fog, debris, and other nonconductive materials. Photo by Ryan Hoover / UW ECE[/caption]
UW ECE Assistant Professor Hossein Naghavi is leading a multi-institutional research project selected for the U.S. Department of Energy’s Genesis Mission, a historic national initiative aimed at building the world’s most powerful integrated science discovery platform. His project is one of only 278 selected nationwide from more than 5,000 applicants, the largest response to a funding opportunity in DOE history. The selected projects were announced on July 22 at the Genesis Mission Summit in Washington, D.C.
Naghavi’s project, “Neuromorphic Terahertz Imaging via Analog Compute-in-Memory in AI-Driven Augmented Reality Hardware,” is focused on developing a low-power, high-bandwidth augmented reality headset that combines terahertz imaging with intelligent sensing and computing. Terahertz waves sit on the electromagnetic spectrum between microwave and optical frequencies. They can be used to see through many nonconductive materials and identify substances based on unique wave absorption and reflection signatures. The headset would enable the user to see through smoke, fog, debris, and other nonconductive matter. Potential applications include firefighting, emergency response, autonomous navigation, security screening, industrial inspection, biomedical sensing, and beyond 5G communication networks.
“I am honored to represent the University of Washington as part of the DOE’s Genesis Mission,” Naghavi said. “This is an exciting project that is rethinking how intelligent sensors are built, and by doing so, the research is supporting national priorities in energy-efficient computing and next-generation hardware.”
According to the DOE, the Genesis Mission was designed to address some of the nation’s most pressing energy, scientific, and engineering challenges while doubling America’s scientific productivity. By uniting government, industry, academia, and philanthropy, the initiative accelerates breakthroughs in energy, scientific discovery, and national security through a new platform combining AI, supercomputing, quantum systems, and advanced scientific instruments.
[caption id="attachment_41763" align="alignleft" width="430"] The U.S. Department of Energy’s Genesis Mission is a historic national initiative aimed at building the world’s most powerful integrated science discovery platform.[/caption]
Projects under the Genesis Mission are collaborative by design; teams must draw on the expertise of researchers from academia, industry, and/or national laboratories. Naghavi’s co-investigators include Milad Koohi, an assistant professor of electrical and computer engineering at Texas A&M University, Morteza Fayazi, an assistant professor of electrical and computer engineering at the University of Utah, and Daniel Elmhurst, chief executive officer of ChipNexus (formerly PrimisAI). The group is also collaborating with John Josephakis, global vice president of high-performance computing and supercomputing at Nvidia.
Naghavi and his team have been selected by the DOE under the Genesis Mission for Phase I funding. During this nine-month phase, the team will design and demonstrate a research workflow that integrates AI with scientific investigation. The DOE will evaluate whether the approach can accelerate discovery, improve predictive capabilities, enhance experimentation, and generate new scientific insights. Projects demonstrating strong potential for transformative scientific capabilities may be considered for additional Genesis Mission funding.
Naghavi said that the nine-month timeframe was ambitious, but he and his colleagues were up to the challenge.
“This project brings together expertise in terahertz systems, semiconductor devices, integrated microsystems, AI methods/hardware, and high-performance computing,” Naghavi said. “By combining those strengths, we can move much faster toward a practical solution than any one institution could alone.”
Read this DOE press release to learn more about the first Genesis Mission projects selected to accelerate AI-driven scientific discovery.
[post_title] => UW ECE-led project selected for Department of Energy’s Genesis Mission
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[post_title] => UW ECE introduces virtual reality training for students to help fill crucial semiconductor jobs
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