About

I recently completed my PhD in Computational Cognitive Science at MIT, supervised by Josh Tenenbaum. I spent the first three years mostly developing Bayesian machine learning methods for explainable AI, then the next three years studying attitude formation, political persuasion, and basically how to elect Democrats.

lhewitt@protonmail.com  |  LinkedIn  |  Google Scholar   

 

Academic stuff

Rank-heterogeneous effects of political messaging (In review, Political Behavior. preprint)
Hewitt and Tappin

Persuasion in political advertising, meta-analysis of 617 treatments (APSA 2022. In review, Science)
Hewitt, Broockman, Coppock, Tappin

Hybrid memoised wake-sleep: paper (ICLR 2022)
Le et al.

Persistence of party cues: paper (JEPS 2021)
Tappin and Hewitt

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

Variational Homoencoder: GitHubpaperoral presentation (UAI 2018)
Hewitt, Nye, Gane, Jaakkola, Tenenbaum

Bayesian Auditory Scene Analysis: websitepaperoral presentation (CogSci 2018)
Cusimano, Hewitt, Tenenbaum, McDermott

 

 


 

( Figure from Memoised wake-sleep, UAI 2020 )