I’m a PhD candidate in the MIT Department of Brain and Cognitive Sciences, supervised by Josh Tenenbaum. My research synthesises Bayesian machine learning and cognitive science to develop learning systems which are more flexible, interpretable, and human-like. I believe this goal is best realised by models which embody explicit structured relationships between parts - compositionality, hierarchy, causality. Given this perspective, I aim to combine probabilistic programming (for its rich knowledge representation) with deep learning and evolutionary computation (for tractable search and inference).
Machine Learning Research Projects
DreamCoder: paper (PLDI 2021)
Ellis et al.
Memoised wake-sleep: paper (UAI 2020)
Hewitt, Le, Tenenbaum
SketchAdapt: paper (ICML 2019)
Nye, Hewitt, Tenenbaum, Solar-Lezama
Inferring Structured Visual Concepts from Minimal Data: paper (CogSci 2019)
Qian, Hewitt, Tenenbaum, Levy
Tutorial on Bayesian Inference In Generative Models: video (CBMM, 2018)
with Maddie Cusimano
Some old writing on AI: [thinking machines]
Predicting emojis [ :-) ]
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,