Machine Learning

Machine learning, speech/language/bioinformatics/music, submodularity & discrete optimization

Machine learning, artificial intelligence, data science

Artificial intelligence, web search

Computer vision, machine learning

Convex optimization; systems and control theory

Robotics, artificial intelligence, activity recognition

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

Computational biology - precision medicine, network biology, genetics of complex traits; Machine learning - interpretability, feature selection, structure learning

Statistical learning algorithms

Development of machine learning techniques for molecular biology

Signal and image processing

Computational neuroscience, artificial intelligence, brain-computer interfaces

Computer vision, multimedia retrieval, biomedical informatics

Artificial intelligence, human computer interaction, natural language processing

Intelligent control in biology and engineering

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.

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.

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).

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.

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.

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.