People

Machine learning, speech/language/bioinformatics/music, submodularity & discrete optimization

Computer vision, machine learning

Convex optimization; systems and control theory

Robotics, artificial intelligence, activity recognition

Machine learning, active learning, reinforcement learning

Computational biology - precision medicine, network biology, genetics of complex traits; Machine learning - interpretability, feature selection, structure learning

Statistical learning algorithms

Development of machine learning techniques for molecular biology

Scientific-discovery games, games for learning, computer graphics, animation, optimal control, natural locomotion, optimization

Computational neuroscience, artificial intelligence, brain-computer interfaces

Computer vision, computer graphics

Computer vision, multimedia retrieval, biomedical informatics

Robotic manipulation, motion planning, human-robot interaction, assistive robotics

Liveness in programming environments, programming for virtual reality, educational technology, collaborative problem-solving environments

Data management, artificial intelligence

Adaptive systems and interaction

Knowledge representation and reasoning systems.

Robotics, brain-machine interface

Human activity recognition, sensor-based reasoning

Human-Computer Interaction and Brain-Computer Interfaces

Intelligent control in biology and engineering

Markus Grotz currently works with Dieter Fox in the UW Robotics and State Estimation Lab and Tamim Asfour in the High Performance Humanoid Technologies Lab (H2T). His research focuses on visual perception for robotic manipulation tasks.

Sasha Lambert works with professor Byron Boots on developing and applying algorithms for perception and imitation learning. These approaches are being tested for off-road autonomous driving as well as and robot manipulation.

Michael Regan works with professor Yejin Choi studying cognitive semantic approaches to the modeling of causal structure in language. His research focuses on creating datasets for model evaluation across a variety of tasks (e.g., schema induction, causal reasoning), problems in event, narrative, and dialogue understanding, and functional approaches to studies of language which hold that physical models of the world shape both human cognition (e.g., intuitive physics) and linguistic constructions, form-meaning pairs where meaning is assumed to be causal. He holds a Ph.D. from the University of New Mexico.

I am an incoming (Fall 2023) Assistant Professor in the Robotics Institute at CMU. I completed my Ph.D. in 2022 from Caltech. I am broadly interested in the intersection of machine learning and control theory, spanning the entire spectrum from theory to real-world agile robotics.

Wenya is a research fellow in Paul G. Allen School of Computer Science and Engineering at the University of Washington, advised by Noah Smith and Hanna Hajishirzi. Her research interests lie in deep learning, logic reasoning and their applications in natural language processing, e.g., information extraction, natural language understanding etc.

Sean Welleck is a Postdoctoral Scholar at UW, working with Yejin Choi. His research interests include deep learning and structured prediction, with applications in natural language processing. He completed his Ph.D. at New York University, advised by Kyunghyun Cho and Zheng Zhang, and obtained B.S.E. and M.S.E. degrees from the University of Pennsylvania. His research has been published at ICML, NeurIPS, ICLR, and ACL, including two Nvidia AI Labs Pioneering Research Awards.

Tao Yu works with professor Noah Smith and Mari Ostendorf of the UW Natural Language Processing group. His research focuses on designing and building conversational natural language interfaces (NLIs) that can help humans explore and reason over data in any application (e.g., relational databases and mobile apps) in a robust and trusted manner. Tao completed his Ph.D. from Yale University.

3D reconstruction and mapping with RGB-D (Kinect-style) cameras.

Natural language processing, artificial intelligence, natural language semantics

I'm interested in natural language understanding. My research is aimed at learning to automatically map natural language sentences to graph representations of their meaning, ideally in a way that works well for a broad variety of domains and languages.

Computer vision