Faculty

EEB-418
bilmescs.washington.edu
Adjunct, Electrical & Computer Engineering

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

CSE2 210
bbootscs.washington.edu
Fundamental and applied research at the intersection of artificial intelligence, machine learning, and robotics
CSE2 236
mcakmakcs.washington.edu
Human-robot interaction, programming by demonstration, robot teleoperation
aylinuw.edu
Adjunct, Information School
Artificial intelligence, AI ethics, algorithmic bias, computer vision, data science, implicit machine cognition, machine learning, natural language processing
rcalouw.edu
Adjunct, School of Law
Law and emerging technology, especially robotics, artificial intelligence, augmented and virtual reality, disinformation, security, and privacy
CSE 578
yejincs.washington.edu
Natural language processing
ssducs.washington.edu
Deep learning, representation learning, reinforcement learning, non-convex optimization
CSE2 203
alics.washington.edu

Computer vision, machine learning

mfazelee.washington.edu
Adjunct, Electrical & Computer Engineering

Convex optimization; systems and control theory

CSE2 204
foxcs.washington.edu

Robotics, artificial intelligence, activity recognition

CSE 528
mgolubcs.washington.edu
Machine learning and data science for neuroengineering and basic systems neuroscience; deep learning techniques for understanding neural computations in the brain; brain-computer interfaces
abhguptacs.washington.edu
Deep reinforcement learning algorithms for robotic systems, with a focus on reward specification, continual real-world data collection and learning, offline reinforcement learning, and multi-task learning and dexterous manipulation
CSE 470
hannanehcs.washington.edu
Natural Language Processing, Artificial Intelligence, Machine Learning
lalitjuw.edu
Adjunct, Foster School of Business
Machine learning, online experiments, human preference learning
CSE2 340
jamiesoncs.washington.edu

Machine learning, active learning, reinforcement learning

njcs.washington.edu
Arriving January 2024
Social reinforcement learning: developing algorithms that combine insights from social learning, deep learning, and multi-agent training to improve AI agents' learning, generalization, coordination, and human-AI interaction.
pangweics.washington.edu
Arriving Fall 2023
Techniques and theory for building reliable and interactive machine learning systems
ranjaycs.washington.edu
Development of new representations, models, and training paradigms for machine learning and computer vision, drawing on insights from human-computer interaction, social, and behavioral sciences
CSE 536
suinleecs.washington.edu

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

CSE346
mmpstat.washington.edu
Adjunct, Statistics

Statistical learning algorithms

saramoscs.washington.edu
Development and application of machine learning and statistical methods to study health and disease
Genome Sciences
william-nobleuw.edu
Adjunct, Genome Sciences

Development of machine learning techniques for molecular biology

CSE2 207
sewoongcs.washington.edu
Theory and practice of machine learning, including generative adversarial networks, differential privacy, anonymous messaging, crowdsourcing, and ranking
CSE 590
zorancs.washington.edu

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

raocs.washington.edu

Computational neuroscience, artificial intelligence, brain-computer interfaces

ratliffluw.edu
Adjunct, Electrical & Computer Engineering
Machine learning, game theory, decision-making, optimization, artificial intelligence
CSE2 342
seitzcs.washington.edu

Computer vision, computer graphics

chiragsuw.edu
Adjunct, Information School
Artificial intelligence, machine learning, data science, information retrieval
CSE 634
shapirocs.washington.edu

Computer vision, multimedia retrieval, biomedical informatics

nasmithcs.washington.edu
Natural language processing
CSE2 242
siddhcs.washington.edu

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

CSE 638
tanimotocs.washington.edu

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

CSE 566
yuliatscs.washington.edu
Natural language processing
swangcs.washington.edu
Computational biology — learning in the open-world setting, biomedical natural language processing, network biology

Faculty (non-CSE)

thomasru.washington.edu
Statistics

Affiliate Faculty

CSE446
lfbcs.washington.edu
Machine Learning, Computer Vision, Robotics
guestrinstanford.edu
Machine learning
halevygoogle.com
Google

Data management, artificial intelligence

horvitzmicrosoft.com
Microsoft Research

Adaptive systems and interaction

kautzcs.rochester.edu
University of Rochester

Knowledge representation and reasoning systems.

Yoky Matsuoka
CSE650
yokycs.washington.edu
Nest

Robotics, brain-machine interface

CSE436
matthaimicrosoft.com
Microsoft Research

Human activity recognition, sensor-based reasoning

desneymicrosoft.com
Microsoft Research

Human-Computer Interaction and Brain-Computer Interfaces

CSE 434
todorovcs.washington.edu

Intelligent control in biology and engineering

Postdocs

grotzcs.washington.edu

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.

lambert6cs.washington.edu

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
mregancs.washington.edu

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.

guanyascs.washington.edu

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.

Ruosong Wang
ruosongwcs.washington.edu
wwenyacs.washington.edu

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.

welleckscs.washington.edu

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.

taoydscs.washington.edu

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.

Graduate Students (CSE)

CSE382
bansalgcs.washington.edu
CSE410
jbarecs.washington.edu
Computational cognitive/neuro science
CSE 510
antoinebcs.washington.edu
Arunkumar Byravan
baruncs.washington.edu
CSE 503
safiyecs.washington.edu
Machine Learning, Computational Biology
CSE 402
tqchencs.washington.edu
CSE482
mjyccs.washington.edu
human-robot interaction, machine learning, brain-computer interface
CSE 374
mbforbescs.washington.edu
Natural language processing: learning commonsense knowledge, NLP + {robotics, vision}
CSE491 (lab)
petercs.washington.edu

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

Justin Huang
jstncs.washington.edu
mandar90cs.washington.edu
natural language processing, machine learning
CSE374
mkochcs.washington.edu
Machine learning, artificial intelligence, natural language processing, information extraction
CSE 386
kentonlcs.washington.edu
natural language processing; artificial intelligence
Julian Michael
julianjmcs.washington.edu

Natural language processing, artificial intelligence, natural language semantics

George Mulcaire
CSE 394
gmulccs.washington.edu
Natural language processing, artificial intelligence
Eric Rombokas
eric.rombokasgmail.com
msapcs.washington.edu
Natural Language Processing, Computational Social Science
CSE 390
samtcs.washington.edu

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.

W Austin Webb
CSE524
webbcs.washington.edu
Congle Zhang
clzhangcs.washington.edu

Non-CSE Grad Students

Collaborators

sumitbmicrosoft.com
Microsoft Research
petercvulcan.com
Vulcan
sdumaismicrosoft.com
Microsoft Research
benjamin.grosofgmail.com
Vulcan
mepatrickpantel.com
Microsoft Research
teevanmicrosoft.com
Microsoft Research

Undergraduate Researchers

Matthew Bryan
CSE 286
mmattbcs.washington.edu
Application of machine learning techniques to the brain-computer interface (BCI) domain. This includes hierarchical task modelling (HBCIs), and the use of POMDPs for optimal data collection.
Maxwell Forbes
mbforbescs.washington.edu
Stefan Martin
CSE 286
stefan7uw.edu
Spatial filtering for the brain-computer interface (BCI) domain.

Alumni

CSE446
lfbcs.washington.edu
Intel Labs
Machine Learning, Computer Vision, Robotics
Marc Deisenroth
CSE480
marccs.washington.edu
CSE394
bdferriscs.washington.edu
CSE506
tlincs.washington.edu
CSE414
cynthiacs.washington.edu
Human-Robot Interaction, Robots and Language, Robot Learning
CSE444
xmcs.washington.edu