I'm a 5th year graduate student in the physics department at Harvard University. My research is at the intersection of machine learning and computational cognitive science, both on a) leveraging methods from machine learning to shed light on process-level accounts of how humans make inferences, and b) using frameworks from cognitive science to build a better understanding of black-box machine learning algorithms.
I work with Sam Gershman in the Computational Cognitive Neuroscience Lab at Harvard. My thesis is on trying to understand how humans infer probabilities in the real world. Specifically, how people might be trading-off the precision/accuracy and the computational costs of various algorithms for statistical inference. I study this by building computational models that make predictions about human behavior in various tasks, and subsequently testing these predictions with online behavioral experiments.
I am also interested more broadly in bounded optimality and resource-rational inference algorithms from a machine learning perspective, as well as in using insights from cognitive science to build more powerful algorithms for AI.