Faculty

CSE2 313
althoffcs.washington.edu
Data Science, Data Mining, Social Network Analysis, Natural Language Processing
saravkinuw.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
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
leobixuw.edu
Adjunct, Foster School of Business
Artificial intelligence, machine learning, operations research, information systems, large language models and generative AI, multimodality, healthcare, environmental sustainability, predictive and prescriptive analytics, AI for social good, AI and the future of work
aylinuw.edu
Adjunct, Information School
Artificial intelligence, AI ethics, algorithmic bias, computational social science, computer vision, data science, machine learning, natural language processing
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 568
gshyamcs.washington.edu

Computational health, AI for sound, networks, bio-robotics, wireless, mobile and ubiquitous computing, sensing, security and privacy

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
PDL B-314
zaiduw.edu
Adjunct, Statistics
Machine learning, mathematical optimization, statistical hypothesis testing, computer vision, and signal processing.
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
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
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

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

armitauw.edu
Adjunct, Physics
Biological physics, physics-guided machine learning, equivariant neural networks, control theory, computational biology, protein science, immunology
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
msaveskiuw.edu
Adjunct, Information School
Computational social science, social networks, causal inference, data mining
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
rbscs.washington.edu
Allen School Associate Director for Diversity, Equity, Inclusion and Access and Professor of Computer Science & Engineering
Computing education research and learning technologies to help people explore their curiosities and create things to improve the world around themselves
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

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
guestrinstanford.edu
Machine learning
CSE 434
todorovcs.washington.edu

Intelligent control in biology and engineering

Postdocs

Alexandra (Sasha) Portnova
aport6cs.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.

Alexander Sasse
CSE 270
asassecs.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.

mjsong32cs.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.

xzhoucs.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
prestonjcs.washington.edu
Computational neuroscience
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
Lillian Li
CSE 306
yli2244cs.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

Staff

Undergraduate Researchers

Matthew Bryan
CSE 286
mmattbcs.washington.edu
Brain-Computer Interfaces, Neural Engineering
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