This page requires that JavaScript be enabled in your browser


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.

CSE2 262

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.

Marshall Ball
Nick Bolten

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

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.

Dharma Dailey is a UW Data Science Postdoctoral Fellow who works with research scientist Anissa Tanweer at the eScience Institute to understand how human centered design practices can be incorporated into data intensive research.

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.

Ares Fisher

Joseph Jaeger works with professor Stefano Tessaro in the Cryptography Group. He primarily aims to use cryptography to find practical solutions to real-world security problems (though he can occasionally be found attempting to resolve questions of more theoretical interest). His research has included secure messaging, time-memory tradeoffs for the security of encryption, and security against mass surveillance. He received his Ph.D. in 2019 from the University of California, San Diego.

Shagun’s research focuses on online harassment and content moderation on social media websites. His dissertation investigated how moderation systems on Twitter and Reddit work, and how they affect platform owners, content moderators, and end-users. His most recent research evaluates the effects of banning or quarantining Reddit communities, examines the potential of a restorative justice approach to addressing online harassment, and analyzes the moderation mechanisms that curate Facebook ads.

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.

CSE 456

Tianren Liu works with professor Huijia (Rachel) Lin in the Cryptography group. His research focuses on information theoretic cryptography, secret sharing (mainly IT), multi-party computation (both IT and computational), and the (im)possibility of basing cryptography on NP-hardness. Tianren received his Ph.D. in 2019 from MIT working with the Theory of Computation group.

CSE2 60B

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.

CSE2 259

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.

CSE2 233

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.

Keisuke Motone
CSE2 333

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.

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.

CSE 410

Nikolaos Pappas works with professor Noah Smith in the Natural Language Processing group. Pappas is interested in creating unified, structure-aware, and sample efficient models of natural language. Previously, he did a postdoc working with James Henderson at the Idiap Research Institute's natural language understanding group and earned his doctoral degree in Electrical Engineering at EPFL.

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.

CSE 280

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.

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.

Amirezza Shaban works with professor Byron Boots on fundamental and empirical machine vision algorithms in robotics. He received his Ph.D. in Computer Science from Georgia Institute of Technology.

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 PhD 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.

Xingyao Zhang

Xingyao Zhang earned his Ph.D. degree in Electrical and Computer Engineering from the University of Houston, Houston, TX, in 2020. He is currently a postdoctoral scholar in the Bespoke Silicon Group lead by Prof. Michael Taylor. His research interest lies in high-performance computing, especially the computer architecture design for machine learning accelerations.