I'm a PhD candidate in the MIT Department of Brain and Cognitive Sciences, supervised by Josh Tenenbaum. My research centers on structured Bayesian modelling for concept learning and perception, both with the goal to develop better computational models of human cognition and to develop more interpretable and robust machine intelligence. My technical interests include probabilistic programming (for specifying knowledge) and deep learning and meta-evolutionary algorithms (for using it).
That said, I am increasingly worried about the implications of machine intelligence, and the ways that it's likely be used by the society it comes of age in. I think it's well-justified to be very concerned about things like rapid unemployment, data privacy, computational propaganda, and particularly the interaction of these against a growing backdrop of authoritarian politics, economic inequality, and ecological crises. If you're thinking about how to increase the probability that AI ends up being a net-positive for the world, I'd really like to hear from you.
Talks, papers, etc.
Memoised wake-sleep: paper (in submission, NeurIPS 2019)
with Josh Tenenbaum
SketchAdapt: paper (ICML 2019)
with Max Nye, Josh Tenenbaum, Armando Solar-Lezama
Inferring Structured Visual Concepts from Minimal Data: paper (CogSci 2019)
with Peng Qian, Josh Tenenbaum, Roger Levy
Tutorial on Bayesian Inference In Generative Models: video (CBMM, 2018)
with Maddie Cusimano
Writing on AI: [thinking machines]
Awards & Fellowships
2019 Walle Nauta Award for Continued Dedication to Teaching, MIT
2018 Angus MacDonald Award for Excellence in Undergraduate Teaching, MIT
2017 Angus MacDonald Award for Excellence in Undergraduate Teaching, MIT
2016 Second place finalist, Flame Challenge for science communication.
2015-2017 Henry E. Singleton (1940) Fellowship, MIT
2015 Dean’s List Commendation for Outstanding Academic Performance, UCL
2014 Vacation Bursary Award,