I led the perception and reinforcement learning team made up of 16 scientists and engineers. The team developed the segmentation, object detection, object tracking, and ML planning components for the Scout robot.

Previously, I was at DeepMind, leading the London applied research team, where we applied state of the art machine learning to high impact products at Google. I managed a team of 12 research scientists and engineers, whose goal was to improve products by applying cutting edge research and perform research on the challenges preventing us from deploying more ML to products. I managed and mentored my team members, set the research direction of the larger team, and led multiple research and product projects. I worked on areas including recommender systems, industrial controls, maps, and robotics.

Before that I was at Nest, where I developed learning algorithms that ran on-board the thermostat such as Auto-Schedule (learning a user's temperature schedule from dial changes), learning a thermal model of the home, and using that model plus utility rate plans to optimally plan HVAC control (e.g. learning to pre-cool the home before rates increased).

My Ph.D. research focused on enabling robots to learn and adapt on-line to new tasks using reinforcement learning (RL). Performing RL on robots poses significant challenges for existing RL algorithms; such as learning in few samples, in real-time, and handling noise and delays on sensors and actuators. My thesis presented an RL algorithm called TEXPLORE which addressed these challenges and was tested on two robot platforms. I also participated in RoboCup, the international robot soccer competition where teams program robots to play soccer autonomously. I worked on all the aspects of the robot soccer problem: computer vision, localization, ball tracking, opponent tracking, humanoid motion, and multi-robot coordination. In 2012, our team won the international RoboCup competition from a field of 25 teams.