Nigini Abilio Oliveira works with professor Katharina Reinecke to improve the user experience - for both researchers and volunteers - in the context of large-scale, volunteer-based online studies. His research interest is in the broad Human-Computer Interaction area focusing on online collaboration, cross-cultural studies, open science, and community design. Nigini has a Ph.D. in Computer Science from the Universidade Federal de Campina Grande in Brazil.
Patricia Alves-Oliveira works with professor Maya Cakmak in the Human-Centered Robotics Lab. Her research lies at the intersection of psychology, design research, and engineering-related fields. She is particularly interested in the design and evaluation of human-centric, long-term interactions with robots. Alves-Oliveira earned her Ph.D. as part of a multidisciplinary international program supported by Foundation for Science and Technology of Portugal (FCT) through ISCTE-IUL, INESC-ID, and Cornell University.
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.
Waylon Brunette works with Richard Anderson in the ICTD Lab. His research focuses on designing, building, and evaluating resilient mobile technologies optimized for usage in resource-constrained and infrastructure-constrained environments. His interdisciplinary research focuses on technology for global health, humanitarian assistance, and international development. He received his Ph.D. from the Allen School in 2020 and is one of the Open Data Kit (ODK) project founders.
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).
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.
Karthik Desingh works with Prof. Dieter Fox in the Robotics and State Estimation Lab. His research interests lie primarily in perception for goal-driven mobile manipulation tasks, specifically representations that can enable robots to perceive objects in the cluttered indoor environments for grasping and manipulation tasks. Desingh completed his Ph.D. in Computer Science and Engineering from the University of Michigan, where he was closely associated with the Robotics Institute and Michigan AI.
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).
Pardis is currently a postdoctoral scholar working with Tadayoshi Kohno and Franziska Roesner. She received a bachelor's degree in computer engineering from Sharif University of Technology, and M.S. and Ph.D. degrees in computer science from Carnegie Mellon University. As part of her doctoral research, she developed a usable privacy and security label for smart devices to inform consumers’ Internet of Things-related purchase decisions.
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.
Ravi Karkar works with James Fogarty and Gary Hsieh. His research focuses on designing, building, and evaluating new personal health technologies. He received his Ph.D. from the Allen School in 2020.
Kendall Lowrey works with Sham Kakade at the intersection of intelligent control and machine learning for robotics applications. His recent work attempts to use complexity as an invariant to automatically discover abstractions in complex dynamics. He received his Ph.D. from the University of Washington in 2019.
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.
Peter Ney is a member of both the Security and Privacy Research Lab and the Molecular Information Systems Lab. His research is focused on understanding computer security risks in emerging technologies like DNA synthesis and sequencing and on developing technologies to detect and measure cell phone surveillance. He earned his Ph.D. in Computer Science & Engineering from the University of Washington.
Adam Richie-Halford works with professor Ariel Rokem in the eScience Institute on the development of statistical learning techniques and software for the analysis of neuroimaging data. Adam's current research interests lie in extracting the biophysical properties of the brain's major white matter connections by leveraging large open datasets containing diffusion MRI images. He earned a Ph.D. in physics from the University of Washington.
Dustin Richmond works with professor Michael Taylor of the Bespoke Silicon Group. His research focuses on the development of emulation tools for a prototype manycore chip on commercial cloud infrastructure, techniques for efficient computation on massively-manycore systems, and information side-channels in cloud-resident custom hardware infrastructure. Dustin earned his Ph.D. from the University of California, San Diego.
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.
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.
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.
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.