I am currently working as a post-doc with Pieter Abbeel
as part of the Berkeley Artificial Intelligence Research Laboratory.
My research lies at the intersection of robotics, machine learning, and computer vision.
I am interested in developing methods for robotic perception and control that can allow robots to operate in in the messy, cluttered environments of our daily lives. My approach is to design new deep learning / machine learning algorithms to understand environmental changes: how dynamic objects in the environment can move and how to affect the environment to achieve a desired task.
I have applied this idea of learning to understand environmental changes to
improve a robot's capabilities in two domains: autonomous driving and object
manipulation. Within autonomous driving, I have shown how, by modeling object
appearance changes, we can improve a robot's perception capabilities for every
part of the robot perception pipeline: segmentation, tracking,
velocity estimation, and object recognition. I am also currently working on
learning to control indoor robots for object manipulation tasks. By teaching
robots to understand and affect environmental changes, I hope to open the door
to many new robotics applications, such as robots for our homes, assisted living
facilities, schools, hospitals, or disaster relief areas.
I recently completed my Ph.D. in computer science at Stanford working with Sebastian Thrun and Silvio Savarese.
Prior to that I did research at the Weizmann Institute, worked in industry as a software developer, and received a B.S. and M.S.
in mechanical engineering at MIT.
You can also download my CV.
Best Master's Thesis of 2012 in Stanford's Computer Science Department
M.S. Thesis: "Autonomous Driving: Car Detection, Tracking, and Street Sign Detection,"
and Sebastian Thrun.