Luke Hewitt
I work on computational & experimental tools for measuring what changes people’s beliefs/attitudes, with application to public health communication, effective advocacy, social science methodology, and AI safety. My research combines RCTs, LLMs, expert forecasting and hierarchical Bayesian models.
Currently:
- I’m a Senior Research Fellow at Stanford PASCL, where I study the capacity of Large Language Models to predict treatment effects in social/behavioral sciences.
- I’m co-director of Rhetorical Labs, a research collective which uses RCT experiments and machine learning to help public communication campaigns improve the impact of their messaging.
- I’m co-PI for the SSRC Mercury Project team on Combatting health misinformation with community-crafted messaging.
Previously:
- PhD in AI / Cognitive Science at MIT
- Masters in Mathematical Computation at UCL
- Research data scientist at Swayable (on RCT experiment/analysis methodology)
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Academic research by topic
Persuasion / communication
Political persuasion • How experiments help campaigns persuade voters: evidence from a large archive of campaigns’ own experiments (Hewitt et al. 2024) • Quantifying the persuasive returns to political microtargeting (Tappin et al. 2022) • Rank-heterogeneous effects of political messages: Evidence from randomized survey experiments testing 59 video treatments (Hewitt et al. 2022) • Estimating the Persistence of Party Cue Influence in a Panel Survey Experiment (Tappin et al. 2021)
Public health • Using in-survey randomized controlled trials to support future pandemic response (Tappin & Hewitt 2024)
Machine learning
Deep generative models • Leveraging Large Language Models to Predict Results of Experiments in the Social Sciences (Hewitt*, Ashokkumar* et al., in prep.) • The Variational Homoencoder: Learning to learn high capacity generative models from few examples (Hewitt et al. 2018)
Structured generative models • Hybrid memoised wake-sleep: Approximate inference at the discrete-continuous interface (Le et al. 2022) • Learning to learn generative programs with memoised wake-sleep (Hewitt et al. 2020)
Program synthesis • DreamCoder: Bootstrapping Inductive Program Synthesis with Wake-Sleep Library Learning (Ellis et al. 2021) • Learning to infer program sketches (Nye et al. 2019)
Cognitive science
Emotion • Emotion prediction as computation over a generative Theory of Mind (Houlihan et al. 2023)
Perception • Bayesian auditory scene synthesis explains human perception of illusions and everyday sounds (Cusimano et al. 2023) • Auditory scene analysis as Bayesian inference in sound source models (Cusimano et al. 2017)
Concept learning • Inferring structured visual concepts from minimal data (Qian et al. 2019)