Tony Dear

Tony Dear is interested in the intersection of robotics, locomotion, and machine and reinforcement learning. He is particularly interested in systems for which traditional planning methods may not be suitable due to problem complexity, but whose structure may be amenable to new methods in reinforcement learning (RL) or deep RL. His goal is to make such methods work on real, physical robots, especially in the realm of locomotion.

Dear received his BS in Electrical Engineering and Computer Science from UC Berkeley in 2012. He subsequently received his MS in 2015 and PhD in 2018, both in Robotics from Carnegie Mellon University. He joined Columbia as a faculty member in 2018 and is currently faculty director of the Bridge to MS Program in Computer Science.