Dr. Byron Boots

Professor

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Biography

Byron Boots is the Amazon Professor of Machine Learning at the University of Washington, focusing on integrating machine learning, artificial intelligence, and robotics. His research emphasizes developing theories and systems that tightly amalgamate perception, learning, and control, addressing problems in areas such as computer vision, state estimation, localization, and robotic manipulation. He is involved in the Robot Learning Laboratory and has served as a co-chair for the IEEE Robotics Automation Society Technical Committee on Robot Learning. Before this position at the University of Washington, he was an Assistant Professor at the Georgia Institute of Technology, after completing his Ph.D. at Carnegie Mellon University. His work has garnered numerous awards, including the DARPA Young Faculty Award and an Early Career Award from Robotics: Science and Systems (RSS). With a robust portfolio in one of the most pertinent inter-disciplinary fields, he has contributed extensively to publications and holds substantial recognitions for both his theoretical work and practical implementations in reinforcement learning and robotics. He conducts research aiming to enhance the efficiency and robustness of robotic systems in dynamic environments while optimizing performance through learning algorithms, resulting in substantial advancements in the areas he tackles.

Research Interests

Experience

Amazon Professor of Machine Learning

2022-01-01 — Present

University of Washington • Seattle, WA

Assistant Professor

2014-01-01 — 2022-01-01

Georgia Tech • Atlanta, GA

Awards

#

Amazon Professor Machine Learning

2022-01-01
#

DARPA Young Faculty Award

2022-01-01
#

Systems Paper Award, Finalist

2021-01-01
#

Paper Award, RSS Workshop Geometry Topology Robotics

2021-01-01
#

Early Career Award, Robotics: Science Systems (RSS)

2020-01-01
#

Outstanding Junior Faculty Research Award

2019-01-01
#

Paper of the Year Award (for 2018), International Journal Robotics Research (IJRR)

2019-01-01

Courses

CSE478: Autonomous Robotics CSE/AMATH 579: Intelligent Control Learning Optimization CSEP546: Machine Learning CSE446: Machine Learning CSE599U: Reinforcement Learning CSE599W: Reinforcement Learning

Requirements for University of Washington

Master Program
Requirements
GPA Requirement
Required:3
TOEFL
Total
Required:80
IELTS
Overall
Required:6.5
Duolingo
Overall Score
Required:105
Overall
Required:105
Prerequisites
Bachelor's degree from a regionally accredited college or university
Application Checklist
  • Online application
  • Transcripts
  • Statement of Purpose
  • Letters of Recommendation
  • Resume/CV
  • English proficiency scores (for international students)
Specialization Notes

Standard Graduate School requirements for University of Washington apply to most departments listed unless specified otherwise by the program.