Group

Current

Post-docs

Ph.D. students

Undergraduate students

Former

Post-docs

Ph.D. students

Wilson Yan (December 2024)
Thesis:
Next: Google Deepmind

Sherry Yang (August 2024)
Thesis:
Next: Google Deepmind
Next Next: Assistant Professor at NYU

Olivia Watkins (August 2024)
Thesis:
Next: OpenAI

Hao Liu (May 2024)
Thesis: Towards A Machine Capable of Learning And Discovering Everything
Next: Google

Nikhil Mishra (May 2024)
Thesis: Object-Centric Perception for Real-World Robotics
Next: Covariant

Yuqing Du (December 2023)
Thesis: Human-Centric Reward Design
Next: Google Deepmind

Andrew (YuXuan) Liu (May 2023)
Thesis: Perception for Real-World Robotic Applications
Next: Covariant

Ajay Jain (May 2023)
Thesis: Transferable Generative Models
Next: co-founder Genmo

Kourosh Hakhamaneshi (May 2022)
Thesis: Pre-training Agents for Design Optimization and Control
Next: Anyscale

Xue Bin (Jason) Peng (December 2021)
Thesis: Acquiring Motor Skills Through Motion Imitation and Reinforcement Learning
Next: Assistant Professor Simon Fraser University

Roshan Rao (August 2021)
Thesis: Training, Evaluating, and Understanding Evolutionary Models for Protein Sequences
Next: Facebook AI Research

Aravind Srinivas (August 2021)
Thesis: Representation Learning for Perception and Control
Next: Research Scientist at OpenAI

Abhishek Gupta (August 2021)
Thesis: How to Train Your Robot: Techniques for Enabling Robotic Learning in the Real World
Next: Post-doc at MIT
Next-next: Assistant Professor at University of Washington

Thanard Kurutach (May 2021)
Thesis: Learning, Planning, and Acting with Models
Next: Research Scientist at Cruise

Joshua Achiam (May 2021)
Thesis: Exploration and Safety in Deep Reinforcement Learning
Next: Research Scientist at OpenAI

Gregory Kahn (December 2020)
Thesis: Mobile Robot Learning
Next: stealth start-up

Ignasi Clavera (August 2020)
Thesis: Overcoming Model-Bias in Reinforcement Learning
Next: Research Scientist at Covariant

Coline Devin (August 2020)
Thesis: Compositionality and Modularity for Robot Learning
Next: Research Scientist at Deepmind

Dylan Hadfield-Menell (August 2020)
Thesis: The Principal-Agent Alignment Problem in Artificial Intelligence
Next: Assistant Professor at MIT

Rohin Shah (August 2020)
Thesis: Extracting and Using Preference Information from the State of the World
Next: Research Scientist at Deepmind

Adam Stooke (August 2020)
Thesis: Advancements in Deep Reinforcement Learning: Algorithms and Implementations
Next: Research Scientist at Deepmind

Carlos Florensa Campo (May 2020)
Thesis: What Supervision Scales? Practical Learning Through Interaction
Next: Research Scientist at Covariant.AI

Jonathan Ho (May 2020)
Thesis: Deep Generative Models: Imitation Learning, Image Synthesis, and Compression
Next: Research Scientist at Google Brain (Amsterdam)

Alex Lee (May 2019)
Thesis: Visual Dynamics Models for Robotic Planning and Control
Next: Research Scientist at Deepmind

Sandy Huang (May 2019)
Thesis: Optimizing for Robot Transparency
Next: Research Scientist at Deepmind

Haoran Tang (May 2019)
Thesis: Towards Informed Exploration for Deep Reinforcement Learning
Next: Research Scientist at Covariant.AI

Josh Tobin (May 2019)
Thesis: Real-World Robotics Perception and Control Using Synthetic Data
Next: Research Scientist at OpenAI

Tuomas Haarnoja (December 2018)
Thesis: Acquiring Diverse Robot Skills via Maximum Entropy Deep Reinforcement Learning
Next: Research Scientist at Deepmind

Chelsea Finn (August 2018)
Thesis: Learning to Learn with Gradients
Next: Research Scientist at Google Brain | Post-doc at Berkeley
Next next: Assistant Professor at Stanford University

Bradly Stadie (August 2018)
Thesis: Learning as a Sampling Problem
Next: Post-doc at the University of Toronto

Yan (Rocky) Duan (December 2017)
Thesis: Meta-Learning for Control
Next: Founder / CTO Covariant.AI

John Schulman (December 2016)
Thesis: Optimizing Expectations: From Deep Reinforcement Learning to Stochastic Computation Graphs
Next: Founder / Research Scientist OpenAI

Karthik Narayan (December 2016)
Thesis: Scalable High-Quality 3D Scanning (Thesis talk: pdf, pptx)
Next: Algorithm Developer (aka Quantitative Analyst) at Hudson River Trading LLC

Arjun Singh (August 2016)
Thesis: Benchmarks for Cloud Robotics
Next: Founder / CEO Gradescope

Jeremy Maitin-Shepard (December 2015)
Thesis: Machine Learning and Optimization for Neural Circuit Reconstruction
Next: Google

Tim Hunter (primary advisor: Alex Bayen) (August 2014)
Thesis: Large-Scale, Low-Latency State Estimation Of Cyber-Physical Systems With An Application To Traffic Estimation
Next: DataBricks

Teodor Moldovan (May 2014)
Thesis: Safety, Risk Awareness and Exploration in Reinforcement Learning
Next: Voleon

Woody Hoburg (August 2013)
Thesis: Aircraft Design Optimization as a Geometric Program
Next: One year at Boeing and then Assistant Professor at MIT starting Fall 2014

Aude Hofleitner (primary advisor: Alex Bayen) (May 2013)
Thesis: A Hybrid Approach of Physical Laws and Data-Driven Modeling for Estimation: the Example of Queuing Networks
Next: Facebook

Visitors and Volunteers

Masters students

Undergraduate students