Faculty
CSE2 313
althoff

cs.washington.edu
Data Science, Data Mining, Social Network Analysis, Natural Language Processing
saravkin

uw.edu
Adjunct, Applied Mathematics
Convex and variational analysis, algorithm design and implementation; robust statistics, machine learning, data science, inverse problems, and uncertainty quantification; health metrics, tracking and navigation, seismic imaging, computational finance, neuroscience, and computational medicine
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
aylin

uw.edu
Adjunct, Information School
Artificial intelligence, AI ethics, algorithmic bias, computational social science, computer vision, data science, machine learning, natural language processing
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 568
gshyam

cs.washington.edu
Computational health, AI for sound, networks, bio-robotics, wireless, mobile and ubiquitous computing, sensing, security and privacy
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
PDL B-314
zaid

uw.edu
Adjunct, Statistics
Machine learning, mathematical optimization, statistical hypothesis testing, computer vision, and signal processing.
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
CSE2 316
jamiemmt

cs.washington.edu
Social impact of machine learning and how social behavior influences decision-making systems
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
mo

ee.washington.edu
Adjunct, Electrical & Computer Engineering
Signal and image processing
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
schmidt

cs.washington.edu
Empirical and theoretical foundations of machine learning, often with a focus on datasets and making machine learning more reliable
chirags

uw.edu
Adjunct, Information School
Artificial intelligence, machine learning, data science, information retrieval
rbs

cs.washington.edu
Computing education research and learning technologies to help people explore their curiosities and create things to improve the world around themselves
CSE 634
shapiro

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

cs.washington.edu
Natural language processing
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)
lucagc

u.washington.edu
Applied Physics Lab
ajc

astro.washington.edu
Astronomy
adobra

u.washington.edu
Statistics, Nursing
Padelford Hall A-317
md5

uw.edu
Statistics
graphical models, algebraic statistics, and model selection
jykim

uw.edu
INSER
tylermc

uw.edu
Statistics
raftery

u.washington.edu
Statistics, Sociology
thomasr

u.washington.edu
Statistics
dwitten

u.washington.edu
Biostatistics
fxia

uw.edu
Linguistics
melihay

uw.edu
BHI
Affiliate Faculty
CSE446
lfb

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

stanford.edu
Machine learning
CSE 434
todorov

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

cs.washington.edu
Steve Mussmann works with Kevin Jamieson and Ludwig Schmidt on active learning and adaptive data collection, both from theoretical and empirical angles. Steve is especially interested in designing data collection methods that are efficient enough to power new settings and applications of machine learning. Currently, Steve is interested in understanding active learning for distribution shift and exploring the fundamental possibilities and limitations of active learning. Steve has a Ph.D. in computer science from Stanford University.
Alexandra (Sasha) Portnova
aport6

cs.washington.edu
I am excited about projects where engineering solutions meet medical needs, specifically those that enable individuals with disabilities interact with the world around them in a more inclusive environment. In the past, I have worked on developing affordable and customizable orthotic devices for individuals with spinal cord injuries and attempted to simplify control methods for complex prosthetic hands. As a postdoc at UW, I hope to harness the advancements in metamaterials and smart textiles to create custom solutions for assistance and rehabilitation needs of individuals with disabilities.
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.
Alexander Sasse
CSE 270
asasse

cs.washington.edu
I am interested in applications of Deep Learning and Machine Learning in Genetics. We are trying to learn gene regulatory models from large-scale sequencing data to characterize the genetic sequences that determine molecular phenotypes. We focus on model interpretation to gain a better understanding of the underlying biological mechanisms that control changes in gene expression, and to identify the main trans-acting factors that are involved in these regulatory processes, such as transcription factors and RNA-binding proteins.
mjsong32

cs.washington.edu
Min Jae Song is a postdoctoral scholar at UW, working with Allen School professors Rachel Lin and Jamie Morgenstern. Min Jae's research interests lie at the intersection of theoretical computer science and machine learning, with a recent focus on establishing algorithmic fairness using tools from theoretical computer science. He obtained his Ph.D. in computer science from New York University, under the supervision of Oded Regev and Joan Bruna, where his research focused on the computational complexity of statistical inference.
Ruosong Wang
ruosongw

cs.washington.edu
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.
xzhou

cs.washington.edu
Xinyi Zhou is a postdoctoral scholar working with Allen School professors Tim Althoff and Amy Zhang. Her research interests are broadly in the intersection of data mining, machine learning, and social computing. Her research has and will continue striving to bridge the gap between social theories and (multimodal) ML/AI techniques to comprehend and improve the online information ecosystem that can breed misleading, untrusted, biased, threatening, insecure, and distracting messages.
Graduate Students (CSE)
Preston Jiang
CSE 374
prestonj

cs.washington.edu
Computational neuroscience
CSE382
bansalg

cs.washington.edu
CSE410
jbare

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

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
luheng

cs.washington.edu
Justin Huang
jstn

cs.washington.edu
CSE 402
sviyer

cs.stanford.edu
mandar90

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

cs.washington.edu
Machine learning, artificial intelligence, natural language processing, information extraction
Aapo Kyrola
akyrola

cs.washington.edu
CSE414
anglil

cs.washington.edu
George Mulcaire
CSE 394
gmulc

cs.washington.edu
Natural language processing, artificial intelligence
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
CSE 382
yanchuan

cs.washington.edu
PhD in residence
computational social science, nlp for politics
Non-CSE Grad Students
rkiyer

u.washington.edu
Discrete optimization. Specifically, submodularity and machine learning
karna

uw.edu
(EE)
Staff
Nick Bolten
bolten

cs.washington.edu
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
CSE394
bdferris

cs.washington.edu
CSE506
tlin

cs.washington.edu
CSE444
xm

cs.washington.edu
CSE490
nath

cs.washington.edu