Drew Bagnell

About Drew Bagnell

J. A.(Drew) Bagnell is an associate professor at Carnegie Mellon University’s Robotics Institute and Machine Learning Department. His interests in artificial intelligence range from algorithmic and basic theoretical development to delivering fielded learning-based systems. Bagnell directs the Learning, AI, and Robotics Laboratory (LAIRLab) within the Robotics Institute. Dr. Bagnell is Chief Technology Officer at Aurora https://aurora.tech/, where he works with an amazing team to develop and deliver self-driving, safely, broadly and quickly.Bagnell and his research group have received over a dozen \"best paper\" research awards in both the robotics and machine learning communities including at the International Conference on Machine Learning, Robotics Science and Systems, and International Conference on Robotics and Automation. A few key papers are provided below.Bagnell received the 2016 Ryan Award, Carnegie Mellon’s award for meritorious teaching, and has served as the founding director of the Robotics Institute Summer Scholars program, a summer research experience that has enabled hundreds of undergraduates throughout the world to leap into robotics research. Dr. Bagnell previously worked on perception and autonomy software efforts as a founding member at Uber’s Advanced Technology Group in Pittsburgh in 20••••16. His robotics focus includes machine learning for dexterous manipulation, decision making under uncertainty, self-driving, agile ground and aerial vehicle control, robot perception and computer vision. Prior to joining the faculty, Bagnell received his doctorate as an NSF Graduate Fellow at Carnegie Mellon in 2004 and completed undergraduate studies with highest honors in electrical engineering at the University of Florida.

Experience

1. Chief Scientist

   Aurora

   Jul 2020 — Present

Education

• Carnegie Mellon University

  Doctor of Philosophy (Ph.D.), Robotics

  1999 — 2004

Skills

• Computer Vision
• Perception
• Artificial Intelligence
• Probability Theory
• Machine Learning
• Control Theory
• Mobile Robotics
• Robotics
• Computer Science