Research Experience & Interests

  1. Reinforcement Learning: As data-driven systems are more tightly integrated into society, more and more systems will show behavior as a feedback-based, iterative optimization. Lessons from RL will show us how these play out.
  2. Model-learning for Control: I am fascinated by how data reflects the dynamics of a system and how predicting that data can be used to solve tasks.
  3. Societally Beneficial AI: I have worked with sociologists, lawyers, and technical researchers to understand the normative and societal implications of automation and artificial intelligence.
  4. Novel & Other Robotics: I want to be able to build useful robots from whatever pieces an engineer has.
My CV PhD Thesis Contact Google Scholar Semantic Scholar

Robotics

Intelligent & novel devices to interact with the physical world.

A conceptual rendering of novel microrobot flight trajectories.
A conceptual rendering of novel microrobot flight trajectories.

Machine Learning

The science of using data to decide in the presence of uncertainty.

The optimization landscape with Bayesian Optimization.

Society

Making sure the stakeholders of automation are in the conversation.

A diagram depicting existing fields of socio-technical inquiry in AI
A diagram depicting existing fields of socio-technical inquiry in AI

Papers

Talks

Mentoring

Representative papers where I am a primary contributor are highlighted

Full Page
|
|
[pdf] [arxiv] [code] [video (<5 min)] [talk (>15min)]
No items found.

|
[Watch Me]
[Slides]
No items found.

I have been lucky to work with many brilliant, younger students:

Berkeley Undergrads

Other Students

  • Brian Li (link forthcoming)