Faculty
EEB-418
bilmes

cs.washington.edu
Adjunct, Electrical & Computer Engineering
Machine learning, speech/language/bioinformatics/music, submodularity & discrete optimization
CSE2 210
bboots

cs.washington.edu
Fundamental and applied research at the intersection of artificial intelligence, machine learning, and robotics
CSE2 236
mcakmak

cs.washington.edu
Human-robot interaction, programming by demonstration, robot teleoperation
aylin

uw.edu
Adjunct, Information School
Artificial intelligence, AI ethics, algorithmic bias, computer vision, data science, implicit machine cognition, machine learning, natural language processing
rcalo

uw.edu
Adjunct, School of Law
Law and emerging technology, especially robotics, artificial intelligence, augmented and virtual reality, disinformation, security, and privacy
CSE 578
yejin

cs.washington.edu
Natural language processing
anind

uw.edu
Adjunct, Information School
ssdu

cs.washington.edu
Deep learning, representation learning, reinforcement learning, non-convex optimization
CSE2 203
ali

cs.washington.edu
Computer vision, machine learning
mfazel

ee.washington.edu
Adjunct, Electrical & Computer Engineering
Convex optimization; systems and control theory
CSE2 204
fox

cs.washington.edu
Robotics, artificial intelligence, activity recognition
CSE 528
mgolub

cs.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
abhgupta

cs.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
hannaneh

cs.washington.edu
Natural Language Processing, Artificial Intelligence, Machine Learning
lalitj

uw.edu
Adjunct, Foster School of Business
Machine learning, online experiments, human preference learning
CSE2 340
jamieson

cs.washington.edu
Machine learning, active learning, reinforcement learning
nj

cs.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.
pangwei

cs.washington.edu
Arriving Fall 2023
Techniques and theory for building reliable and interactive machine learning systems
ranjay

cs.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
suinlee

cs.washington.edu
Computational biology - precision medicine, network biology, genetics of complex traits; Machine learning - interpretability, feature selection, structure learning
CSE346
mmp

stat.washington.edu
Adjunct, Statistics
Statistical learning algorithms
saramos

cs.washington.edu
Development and application of machine learning and statistical methods to study health and disease
Genome Sciences
william-noble

uw.edu
Adjunct, Genome Sciences
Development of machine learning techniques for molecular biology
CSE2 207
sewoong

cs.washington.edu
Theory and practice of machine learning, including generative adversarial networks, differential privacy, anonymous messaging, crowdsourcing, and ranking
CSE 590
zoran

cs.washington.edu
Scientific-discovery games, games for learning, computer graphics, animation, optimal control, natural locomotion, optimization
rao

cs.washington.edu
Computational neuroscience, artificial intelligence, brain-computer interfaces
ratliffl

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

cs.washington.edu
Computer vision, computer graphics
chirags

uw.edu
Adjunct, Information School
Artificial intelligence, machine learning, data science, information retrieval
CSE 634
shapiro

cs.washington.edu
Computer vision, multimedia retrieval, biomedical informatics
nasmith

cs.washington.edu
Natural language processing
CSE2 242
siddh

cs.washington.edu
Robotic manipulation, motion planning, human-robot interaction, assistive robotics
CSE 638
tanimoto

cs.washington.edu
Liveness in programming environments, programming for virtual reality, educational technology, collaborative problem-solving environments
CSE 566
yuliats

cs.washington.edu
Natural language processing
swang

cs.washington.edu
Computational biology — learning in the open-world setting, biomedical natural language processing, network biology
CSE 534
lsz

cs.washington.edu
Faculty (non-CSE)
thomasr

u.washington.edu
Statistics
Affiliate Faculty
CSE446
lfb

cs.washington.edu
Machine Learning, Computer Vision, Robotics
guestrin

stanford.edu
Machine learning
halevy

google.com
Google
Data management, artificial intelligence
horvitz

microsoft.com
Microsoft Research
Adaptive systems and interaction
kautz

cs.rochester.edu
University of Rochester
Knowledge representation and reasoning systems.
Yoky Matsuoka
CSE650
yoky

cs.washington.edu
Nest
Robotics, brain-machine interface
CSE436
matthai

microsoft.com
Microsoft Research
Human activity recognition, sensor-based reasoning
desney

microsoft.com
Microsoft Research
Human-Computer Interaction and Brain-Computer Interfaces
CSE 434
todorov

cs.washington.edu
Intelligent control in biology and engineering
Postdocs
grotz

cs.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.
lambert6

cs.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
mregan

cs.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.
guanyas

cs.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
ruosongw

cs.washington.edu
wwenya

cs.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.
wellecks

cs.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.
taoyds

cs.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
bansalg

cs.washington.edu
CSE410
jbare

cs.washington.edu
Computational cognitive/neuro science
CSE 510
antoineb

cs.washington.edu
Arunkumar Byravan
barun

cs.washington.edu
CSE 503
safiye

cs.washington.edu
Machine Learning, Computational Biology
CSE 402
tqchen

cs.washington.edu
eunsol

cs.washington.edu
CSE482
mjyc

cs.washington.edu
human-robot interaction, machine learning, brain-computer interface
nfitz

cs.washington.edu
CSE 374
mbforbes

cs.washington.edu
Natural language processing: learning commonsense knowledge, NLP + {robotics, vision}
luheng

cs.washington.edu
CSE491 (lab)
peter

cs.washington.edu
3D reconstruction and mapping with RGB-D (Kinect-style) cameras.
Justin Huang
jstn

cs.washington.edu
mandar90

cs.washington.edu
natural language processing, machine learning
CSE374
mkoch

cs.washington.edu
Machine learning, artificial intelligence, natural language processing, information extraction
CSE 386
kentonl

cs.washington.edu
natural language processing; artificial intelligence
CSE414
anglil

cs.washington.edu
Julian Michael
julianjm

cs.washington.edu
Natural language processing, artificial intelligence, natural language semantics
George Mulcaire
CSE 394
gmulc

cs.washington.edu
Natural language processing, artificial intelligence
Eric Rombokas
eric.rombokas

gmail.com
msap

cs.washington.edu
Natural Language Processing, Computational Social Science
CSE 390
samt

cs.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
webb

cs.washington.edu
clzhang

cs.washington.edu
Congle Zhang
clzhang

cs.washington.edu
Non-CSE Grad Students
karna

uw.edu
(EE)
Collaborators
sumitb

microsoft.com
Microsoft Research
peterc

vulcan.com
Vulcan
sdumais

microsoft.com
Microsoft Research
benjamin.grosof

gmail.com
Vulcan
me

patrickpantel.com
Microsoft Research
teevan

microsoft.com
Microsoft Research
Undergraduate Researchers
Matthew Bryan
CSE 286
mmattb

cs.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
mbforbes

cs.washington.edu
Stefan Martin
CSE 286
stefan7

uw.edu
Spatial filtering for the brain-computer interface (BCI) domain.
Alumni
CSE446
lfb

cs.washington.edu
Intel Labs
Machine Learning, Computer Vision, Robotics
jbragg

cs.washington.edu
Marc Deisenroth
CSE480
marc

cs.washington.edu
CSE394
bdferris

cs.washington.edu
CSE506
tlin

cs.washington.edu
CSE414
cynthia

cs.washington.edu
Human-Robot Interaction, Robots and Language, Robot Learning
CSE444
xm

cs.washington.edu
CSE490
nath

cs.washington.edu
xiaofeng.ren

Amazon