I’m interested in the relationship between human cognition and artificial intelligence. How do humans learn generalizable representations? Where does human cognition and artificial intelligence differ?
Currently, I’m working on the problem of machine teaching. Expertly trained machine learning models contain the knowledge to solve a specific task nearly perfectly. My goal is to develop teaching algorithms that can transform these uninterpretable expert models into interpretable, personalized teaching material for human students. My intention with this work is to make high-quality tutoring available to students from diverse backgrounds.
I earned a B.S. in Physiology & Neuroscience with a minor in Computer Science from the University of California San Diego. As a student at UCSD, I did systems neuroscience research in the Komiyama Lab for two years and neuroeconomics research with Dr. Pamela Reinagel for one year. During these years, I developed an interest in artificial neural networks and, upon graduation, decided to join the Computational Vision Group at Caltech.