UW ECE team of researchers present their unique 'unsupervised' approach to action recognition this week at major Computer Vision and Pattern Recognition (CVPR) 2020 conference.
Data Sciences are fundamentally transforming nearly every area of engineering, science, and society. The University of Washington’s Electrical & Computer Engineering faculty are making fundamental contributions to many different areas of data sciences, including machine learning, AI, optimization, information theory, computer vision, and speech and natural language processing. Many of our data sciences faculty hold secondary appointments in applied mathematics, computer science and engineering, bioengineering, and other departments, and are active participants in cross-disciplinary institutes such as UW’s eScience Institute, the Allen Institute of Artificial Intelligence and the Bloedel Hearing Research Center.
Artificial intelligence (AI), mathematical optimization and information theory.
Faculty: Katrin Kirchhoff, Jeffrey A. Bilmes, Les Atlas, Maryam Fazel, Sreeram Kannan, Mari Ostendorf, Ming-Ting Sun, Eli Shlizerman, Jenq-Neng Hwang, Linda Shapiro, Hannaneh Hajishirzi, Shwetak Patel, Radha Poovendran
Statistical Signal Processing
Theory, algorithms, signal processing systems and signal processing applications (i.e. biomedical, geophysical signals and synthetic signals).
Speech and Natural Language Processing
Speech recognition, natural language understanding, computational linguistics and web-based language techniques.
Computer Vision and Image Processing
Video analysis, surveillance, object recognition, activity recognition, medical image analysis and video compression
UW ECE faculty and students are leading collaborative research aimed at reducing impacts of the novel coronavirus (COVID-19). Projects range from assisting with diagnostics, testing and tracking, to engineering ventilator technology, to developing targeted treatments for the disease.
System Design Methodologies Professor Mari Ostendorf joins one of eight new corresponding fellows, announced from across sciences, arts, education, business and public life.
ECE lecturer John Raiti's MSTI students created SnapSort!, an intelligent, real-time trash sorting assistant that uses computer vision and machine learning.
Could this be the beginning of a new era of VR and AR tech? ECE doctoral student Farshid Salemi Parizi and Prof. Shwetak Patel have created AuraRing, a 5 DoF electromagnetic ring and wristband combination that can detect the precise location of someone’s finger and continuously track hand movements.
The UW EXP study is a biometric approach to understanding and addressing student wellness.
- Sreeram Kannan
- Eli Shlizerman
- Shwetak N. Patel
- Radha Poovendran
- Ming-Ting Sun
- Linda G. Shapiro
- Eve A. Riskin
- Mari Ostendorf
- Brian A. Nelson
- Katrin Kirchhoff
- Jenq-Neng Hwang
- Hannaneh Hajishirzi
- Maryam Fazel
- Jeffrey A. Bilmes
- Les Atlas
- MachinE Learning, Optimization, and Data Interpretation (MELODI) Laboratory
- Transformation, Interpretation and Analysis of Language (TIAL)
- Intelligent Systems Lab
- Graphics and Imaging Lab
- Data-Driven Dynamical Systems
- Signal, Speech and Language Interpretation Lab
- Silicon System Research Lab
- Ubicomp (Ubiquitous Computing) Research Lab
- Data Compression Lab
- Design, Test and Reliability Research Laboratory
- Information Processing Lab
- Information Theory Lab
- Interactive System Design Laboratory
- Digital Pathology: Accuracy, Viewing Behavior and Image Characterization (with PI: Joann Elmore at Harborview and others)
- 3D Head Reconstruction from Images or Videos (with Ira Kemmelmacher-Shlizerman in CSE)
- Expression Recognition with Deep Neural Nets (with Barbara Mones in CSE)
- Automatic Recognition of Power Line in Millimeter Wave Radar Video
- Surface Light Field Compression Using a Point Cloud Codec
- A Data-driven Point Cloud Simplification Framework for City-scale Image-based Localization
- Full-Capacity Unitary Recurrent Neural Networks
- Deep Submodular Functions: Definitions and Learning
- Exploiting Tradeoffs for Exact Recovery in Heterogeneous Stochastic Block Models
- Y-Net: Joint Segmentation and Classification Diagnosis of Breast Biopsy Images