Faculty

CSE2 313
althoffcs.washington.edu
Data Science, Data Mining, Social Network Analysis, Natural Language Processing
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
CSE 578
yejincs.washington.edu
Natural language processing
CSE 648
pedrodcs.washington.edu
Emeritus

Machine learning, artificial intelligence, data science

ssducs.washington.edu
Deep learning, representation learning, reinforcement learning, non-convex optimization
CSE2 312
etzionics.washington.edu
Emeritus

Artificial intelligence, web search

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 470
hannanehcs.washington.edu
Natural Language Processing, Artificial Intelligence, Machine Learning
PDL B-314
zaiduw.edu
Adjunct, Statistics
Machine learning, mathematical optimization, statistical hypothesis testing, computer vision, and signal processing.
CSE2 340
jamiesoncs.washington.edu

Machine learning, active learning, continuous and discrete optimization, multi-armed bandits, machine learning software systems

CSE2 303
shamcs.washington.edu
Large scale computational methods for statistics, machine learning, signal processing
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

CSE2 316
jamiemmtcs.washington.edu
Social impact of machine learning and how social behavior influences decision-making systems
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
moee.washington.edu
Adjunct, Electrical & Computer Engineering

Signal and image processing

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
ajratnergmail.com
Algorithmic, theoretical, and systems-related techniques for creating and managing training datasets with weak supervision
schmidtcs.washington.edu
Empirical and theoretical foundations of machine learning, often with a focus on datasets and making machine learning more reliable
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
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
CSE 588 (Zoom: 5348583561)
weldcs.washington.edu

Artificial intelligence, human computer interaction, natural language processing

Faculty (non-CSE)

lucagcu.washington.edu
Applied Physics Lab
ajcastro.washington.edu
Astronomy
adobrau.washington.edu
Statistics, Nursing
Padelford Hall A-317
md5uw.edu
Statistics
graphical models, algebraic statistics, and model selection
jykimuw.edu
INSER
tylermcuw.edu
Statistics
rafteryu.washington.edu
Statistics, Sociology
thomasru.washington.edu
Statistics
dwittenu.washington.edu
Biostatistics
fxiauw.edu
Linguistics

Affiliate Faculty

CSE446
lfbcs.washington.edu
Machine Learning, Computer Vision, Robotics
CSE2 336
guestrincs.washington.edu
Machine learning
CSE 434
todorovcs.washington.edu

Intelligent control in biology and engineering

Postdocs

Nick Bolten
boltencs.washington.edu

Nick is a postdoctoral researcher with the Taskar Center for Accessible Technology doing work in mapping and analyzing individual-level pedestrian accessibility of public spaces with graph theoretic interpretations of equity. His doctoral work involved the development of the OpenSidewalks and AccessMap projects that, respectively, define and collect open pedestrian transportation network data for a wide set of use cases and interpret that data into a user-friendly and individually-customizable web map.

sardeancs.washington.edu

Sarah has a Ph.D. in EECS from UC Berkeley, where she was advised by Ben Recht. At UW, she is working with Jamie Morgenstern to connect perspectives on fairness in ad auctions with recommendation systems. More generally, Sarah is interested in the interplay between optimization, machine learning, and dynamics in real-world systems. Her research focuses on understanding the fundamentals of data-driven control and decision-making, broadly categorized into two thrusts: guaranteeing safety in feedback control and ensuring values in social-digital systems.

sydongcs.washington.edu

Siyuan Dong is a postdoc scholar at University of Washington. His current research interest includes robotic manipulation, tactile sensing and machine learning. His works have been nominated for the Best Paper Award in RSS (2020), Best Manipulation Paper Award in ICRA (2020).

schleichcs.washington.edu

Maximilian Schleich works with professor Dan Suciu of the Database group. His research lies at the interface of databases and machine learning. In particular, he investigates how the learning of models can be improved by exploiting the structure and semantics of the underlying database. Schleich received his Ph.D. in Computer Science from the University of Oxford.

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.

Graduate Students (CSE)

CSE382
bansalgcs.washington.edu
CSE410
jbarecs.washington.edu
Computational cognitive/neuro science
CSE 510
antoinebcs.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
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
Aapo Kyrola
akyrolacs.washington.edu
George Mulcaire
CSE 394
gmulccs.washington.edu
Natural language processing, artificial intelligence
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
CSE 382
yanchuancs.washington.edu
PhD in residence
computational social science, nlp for politics

Non-CSE Grad Students

rkiyeru.washington.edu
Discrete optimization. Specifically, submodularity and machine learning

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
CSE394
bdferriscs.washington.edu
CSE506
tlincs.washington.edu
CSE444
xmcs.washington.edu