Adam Fishman
Brian Lee
Jakub Filipek
Rohan Baijal
Tudor Fanaru
Wentao Yuan
Zoey Chen
Rwik Rana is Masters Student in the Mechanical Engineering Department at the University of Washington, working with professor Byron Boots. He wants to become a full-stack roboticist. His research interest is an amalgamation of perception, planning and controls of mobile robots.
Sanghun Jung is a Ph.D. student in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, working with professor Byron Boots. His research interest lies in the intersection of robot perception and control, enabling robots and vehicles to perform robustly in unseen environments. Previously, he received his Master’s degree in Artificial Intelligence from KAIST and his Bachelor’s degree in Computer Science from Korea University.
Guanya is a postdoctoral scholar in the Paul G. Allen School of Computer Science and Engineering at the University of Washington with Prof. Byron Boots. He is an incoming (Fall 2023) Assistant Professor at the Robotics Institute and the School of Computer Science at Carnegie Mellon University (CMU). He completed his Ph.D. in 2022 from Caltech. He is broadly interested in the intersection of machine learning and control theory, spanning the entire spectrum from theory to real-world agile robotics.
Alex is a postdoctorate researcher in the Robot Learning Lab at the University of Washington and simulation lead for the RACER project. He received the PhD from Carnegie Mellon University, where he studied the application of machine learning to agile and accurate quadrotor flight.
Nolan Wagener is a Robotics PhD student in the School of Interactive Computing at Georgia Tech, working with Byron Boots and Panagiotis Tsiotras (Georgia Tech). His research focuses on safe reinforcement learning and model predictive control, with the goal of an agent being able to interact with and learn from an environment with little to no risk of damaging itself.
Xiangyun is a PhD student in the Robust State Estimation Lab at the University of Washington. His research focuses on the perception for robots, with applications in mapping, planning and control.
Sasha is a postdoctoral scholar in the Robot Learning Lab, and completed his Ph.D in Robotics at Georgia Tech. His research interests range from uncertainty-based perception and planning to imitation learning and model-based RL. He works on learning-from-demonstration and inverse reinforcement learning for the RACER project.
Sandeep Reddy is a master’s student in ME Department at the University of Washington. After doing research in multi-robot formation and autonomous navigation at UW-ME, he is currently working on local planning and control at RACER.
Tyler is a new Ph.D. student in the Robot Learning Lab at the University of Washington. Tyler plans to conduct research in the intersection of learning and control.
Matt is a P.h.D. student in the Personal Robotics Lab at the University of Washington. His research focuses on global motion planning for off-road autonomous vehicles with a emphasis on uncertainty, safety, noise, and real world performance.
Roger is a Field Engineer for the Applied Physics Lab at the University of Washington where he serves as the RACER team vehicle hardware specialist. He has 30 years of experience building and driving on and off road race cars and was the 2018 Evergreen Speedway Hall of Fame recipient for his stellar performance as a race driver and car builder.
Eliot is a Senior Mechanical Engineer in the Applied Physics Laboratory at the University of Washington where he works on a variety of autonomous system and oceanographic programs. He turns wrenches, supports field testing and serves as the backup crash test dummy for RACER.
Nathan works on control-related code and is the primary software lead for RACER field testing. He studied robotics and machine learning as a graduate student at the University of Washington and at Georgia Tech.
Sidd Srinivasa is the Boeing Endowed Professor at the Paul G. Allen School of Computer Science at the University of Washington, and the Director of Robotics AI, a ~400 person organization, at Amazon. Prior to that, he spent 18 years at the Robotics Institute at Carnegie Mellon University. Sidd is a full-stack roboticist, an IEEE Fellow, and has authored numerous best-paper winning papers in planning, human-robot interaction, and control. Sidd is also a first-wave founder of Berkshire Grey Inc. which is currently traded on the New York Stock Exchange.
Selest is the Robotics Lab Manager at the Paul G. Allen School of Computer Science and Engineering. He is responsible for over 4,000-square-foot of laboratory space at the University of Washington. He acts as part of the support team for the RACER project and ensures that tasks are done accordingly.
Dieter Fox is Professor in the UW Allen School of Computer Science & Engineering, where he leads the Robotics and State Estimation Lab. Dieter is also Senior Director of Robotics Research at NVIDIA. His research interests are in robotics and AI, with a focus on perception and its connections to state representations and decision making.
Greg is a Principal Engineer at the Applied Physics Laboratory at the University of Washington where he develops autonomous systems, signal processing algorithms, and sensor fusion approaches to solve advanced Department of Defense problems. For RACER he works as a project manager, leads field testing and logistics, and serves as the team’s primary crash test dummy.
Steph leads partnerships and other development activities for the RACER team. She has a decade of experience in software engineering and product management, an MBA from The Wharton School and a BS in Industrial Engineering from Purdue University.
Byron is the Amazon Endowed Professor of Machine Learning and Director of the Robot Learning Lab in the Paul G. Allen School for Computer Science and Engineering at the University of Washington. Byron works on both fundamental and applied research in machine learning, artificial intelligence, and robotics with a focus on developing theory and systems that tightly integrate perception, learning, and control.