Qingqing Cao's research interests include efficient NLP, mobile computing, and machine learning systems. My current focus is building efficient and practical NLP systems for diverse platforms including resource-constrained edge devices and the cloud servers. Previously, I have worked on projects like faster Transformer models for question answering (ACL 2020), as well as accurate and interpretable energy estimation of NLP models (ACL 2021).
My research in HI focuses on making collaborative content production in ability-diverse teams more accessible and equitable. Currently, I am a postdoctoral research fellow at the UW Center for Research and Education on Accessible Technology and Experiences (CREATE). My postdoc research will focus on understanding and building technologies to support how disabled and non-disabled kids learn to make and learn by making together. In Fall 2023, I will join Northeastern University as an Assistant Professor in the Khoury College of Computer Sciences and the College of Arts, Media and Design.
Markus Grotz currently works with Dieter Fox in the UW Robotics and State Estimation Lab and Tamim Asfour in the High Performance Humanoid Technologies Lab (H2T). His research focuses on visual perception for robotic manipulation tasks.
Tianxing He works with Yulia Tsvetkov on natural language generation. He works towards a better understanding of how the current large language models work. Related, he is interested in monitoring and detecting different behaviors of language models under different scenarios, and approaches to fix undesirable behaviors.
I have a background in digital design, and now specialize in tools and cognition. My research interests focus on (equality in) computers and education, creativity research, idea management, personal information management, qualitative research, mixed methods, and methodology in general.
Umar Iqbal researches web privacy and security with an aim to bring more transparency and control to the users. More specifically, he uses internet measurement techniques to audit and quantify malicious practices on the web and leverages ML-based techniques to build privacy-enhancing tools that protect users against malicious practices.
Taylor Kessler Faulkner works with professor Siddhartha Srinivasa in the Personal Robotics Lab. Her research interests include human-robot interaction and AI, and her recent work is on developing interactive algorithms for robots learning from non-expert human teachers. Taylor received her Ph.D. in Computer Science from The University of Texas at Austin.
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.
Christoforos (Chris) Mavrogiannis works with professor Siddhartha Srinivasa in the Personal Robotics Lab. His interests lie at the intersection of robotics, human-robot interaction and artificial intelligence. His recent work focuses on the design of planning algorithms for navigation in multiagent domains such as crowded human environments and street intersections. He holds M.S. and Ph.D. degrees from Cornell University and a Diploma in Mechanical Engineering from the National Technical University of Athens.
Keisuke Motone works with research professor Jeff Nivala in the Molecular Information Systems Lab (MISL). His research focuses on developing chemical and computational approaches to decoding biological information stored within protein and peptide sequences with nanopore sensor technology. Motone completed his Ph.D. in Applied Life Sciences at Kyoto University in Japan.
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.
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 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.
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.
I am an incoming (Fall 2023) Assistant Professor in the Robotics Institute at CMU. I completed my Ph.D. in 2022 from Caltech. I am broadly interested in the intersection of machine learning and control theory, spanning the entire spectrum from theory to real-world agile robotics.
Wenya is a research fellow in Paul G. Allen School of Computer Science and Engineering at the University of Washington, advised by Noah Smith and Hanna Hajishirzi. Her research interests lie in deep learning, logic reasoning and their applications in natural language processing, e.g., information extraction, natural language understanding etc.
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.
Max Willsey earned his Ph.D. at the Allen School and as a postdoc works mostly in programming languages (PLSE group) with Zachary Tatlock but also collaborates with friends in molecular systems (MISL), and machine learning systems (SAMPL). He is currently working on egg, a toolkit for program optimization and synthesis powered by e-graphs and equality saturation.
Momona is a CREATE postdoc working with Prof. Jennifer Mankoff. She completed her Ph.D. in Electrical Engineering at the University of Washington advised by Profs. Sam Burden and Kat Steele in 2022. Her research focuses on modeling and enhancing biosignals-based human-machine interaction to support accessibility and health. She is interested in studying how biosignals can be used to support accessible technology input tailored to an individual’s abilities.
Tao Yu works with professor Noah Smith and Mari Ostendorf of the UW Natural Language Processing group. His research focuses on designing and building conversational natural language interfaces (NLIs) that can help humans explore and reason over data in any application (e.g., relational databases and mobile apps) in a robust and trusted manner. Tao completed his Ph.D. from Yale University.
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.