# Sergey Levine

### Research Areas

- Artificial Intelligence (AI)
- Control, Intelligent Systems, and Robotics (CIR)

### Research Centers

- Berkeley Artificial Intelligence Research Lab (BAIR)
- Berkeley Deep Drive (BDD)
- CITRIS People and Robots (CPAR)

## Biography

Sergey Levine received a BS and MS in Computer Science from Stanford University in 2009, and a Ph.D. in Computer Science from Stanford University in 2014. He joined the faculty of the Department of Electrical Engineering and Computer Sciences at UC Berkeley in fall 2016. His work focuses on machine learning for decision making and control, with an emphasis on deep learning and reinforcement learning algorithms. Applications of his work include autonomous robots and vehicles, as well as computer vision and graphics. His research includes developing algorithms for end-to-end training of deep neural network policies that combine perception and control, scalable algorithms for inverse reinforcement learning, deep reinforcement learning algorithms, and more.

## Education

- 2014, Ph.D., Computer Science, Stanford University
- 2009, B.S/M.S., Computer Science, Stanford University

## Awards, Memberships and Fellowships

- Okawa Research Grant, 2021
- Sloan Research Fellow, 2019
- NSF Faculty Early Career Development Award (CAREER), 2017
- MIT Tech Review Top 35 Innovators Under 35 (TR35), 2016
- Office of Naval Research Young Investigator, 2016