Faculty
CSE2 313
althoff

cs.washington.edu
Data Science, Data Mining, Social Network Analysis, Natural Language Processing
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
CSE 578
yejin

cs.washington.edu
Natural language processing
anind

uw.edu
Adjunct, iSchool
test
CSE 648
pedrod

cs.washington.edu
Emeritus
Machine learning, artificial intelligence, data science
ssdu

cs.washington.edu
Deep learning, representation learning, reinforcement learning, non-convex optimization
CSE2 312
etzioni

cs.washington.edu
Emeritus
Artificial intelligence, web search
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
CSE2 343
ebfox

cs.washington.edu
Large-scale Bayesian dynamic modeling and computations
CSE2 336
guestrin

cs.washington.edu
Machine learning
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.
CSE2 340
jamieson

cs.washington.edu
Machine learning, active learning, continuous and discrete optimization, multi-armed bandits, machine learning software systems
CSE2 303
sham

cs.washington.edu
Large scale computational methods for statistics, machine learning, signal processing
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
ajratner

gmail.com
Algorithmic, theoretical, and systems-related techniques for creating and managing training datasets with weak supervision
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
wxs

stat.washington.edu
Adjunct, Statistics
Computational and graphical methods in multivariate analysis
swang

cs.washington.edu
Computational biology - learning in the open-world setting, biomedical natural language processing, network biology
weld

cs.washington.edu
Artificial intelligence, human computer interaction, natural language processing
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
CSE 434
todorov

cs.washington.edu
Intelligent control in biology and engineering
Postdocs
Nick Bolten
bolten

cs.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.
whkong

cs.washington.edu
Weihao Kong works with professor Sham Kakade. His research focuses on the information-theoretic side of machine learning, with the goal of developing efficient algorithms that extract accurate information from modest amounts of data under varies settings (e.g., high-dimensional, distributed, etc.). More broadly, his research interests span statistical learning, high-dimensional statistics, and theoretical computer science. He earned his Ph.D. from the Computer Science Department at Stanford University.
amrita

cs.washington.edu
Amrita Mazumdar works with Luis Ceze at the intersection of computer systems and visual computing. Her recent work applies ideas from computer vision and perception to cloud software and custom hardware. She received her Ph.D. from the Allen School in 2020.
CSE2 233
jmcmahan

cs.washington.edu
Joseph McMahan works with professor Luis Ceze and the SAMPL group on deep learning research. His current research deals with connecting the rapidly-changing demands of machine learning models with agile hardware support to enable faster and more dynamic ML development and research. His previous research has been at the intersection of computer architecture, formal methods, and security. McMahan received his Ph.D. from UC Santa Barbara in computer architecture.
schleich

cs.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.
Graduate Students (CSE)
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)
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