I am an Assistant Professor of Computer Science at Brown University. In my research, I’m broadly interested in the intersection of computer graphics with artificial intelligence and machine learning. I primarily focus on learning, inference, and synthesis with generative graphics models and programs. Questions I care about in this domain include:
- How can we learn structured generative models of visual content from (small or large) data?
- How can we discover re-usable abstractions in visual content and exploit them in generative models?
- How can we make it easier to create procedural representations of visual content?
- How can we infer procedural representations from unstructured, noisy, perceptual input, such as images?
- How can generative models efficiently incorporate functional constraints, such as physical stability?
I received my PhD from Stanford University, where I worked with Pat Hanrahan in the Graphics Lab and with Noah Goodman in the Computation and Cognition Lab. I received my undergraduate degree from the University of California, Berkeley.