My general interests are very broad — I’m interested in AI, machine learning, programming languages, complexity theory, algorithms, security, quantum computing, you name it. However, I think that it is really important for us to build safe, aligned AI, and so my research focuses on addressing that. Currently, I am working on practical algorithms for inverse reinforcement learning that take into account the systematic cognitive biases that humans have.

In the past, I worked with Ras Bodik in the PLSE lab at the University of Washington. I applied program synthesis techniques to automatically generate incremental update rules that accelerated approximate sampling algorithms used in probabilistic programming. I have also applied partial evaluation and memoization to compile sampling algorithms.

I am currently supported by an NSF Fellowship, and was supported by a Berkeley Fellowship for my first two years in grad school.