Sam Witty
Senior Research Scientist at Toyota Research Institute
City of Ithaca, New York, United States
Summary
👤
Senior🎓
Top SchoolSam Witty is a Senior Research Scientist specializing in causal and probabilistic AI applied to materials science, with nine years of research and industry experience and a PhD in computer science. He has led research at Toyota Research Institute and Basis, founded Sorbus AI as a principal consultant, and collaborated with MIT and the Broad Institute on probabilistic programming and causal inference. Sam combines rigorous probabilistic modeling—demonstrated by QA and gradient-testing contributions to the Gen.jl probabilistic programming system—with practical product and evaluation experience from both startups and industrial research. Based in Ithaca, he bridges deep academic expertise with hands-on engineering, often surfacing subtle numerical issues in probabilistic code that improve model reliability.
9 years of coding experience
12 years of employment as a software developer
Doctor of Philosophy (PhD), Computer Science, Doctor of Philosophy (PhD), Computer Science at University of Massachusetts AmherstENGINEERING, ENGINEERING at Greenfield Community College
English
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (4)
probabilistic-programming10
testing10
machine-learning9
bayesian8
Programming languages (4)
Github contributions (5)
probcomp/Gen.jl
Nov 2019 - Feb 2021
A general-purpose probabilistic programming system with programmable inference
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:13 commits, 4 PRs, 8 comments in 1 year 3 months
Contributions summary:Sam focused on enhancing the testing suite for the `gen.jl` probabilistic programming system. They implemented and extended finite difference tests for the gradient computations within the multivariate normal distribution, specifically checking the gradients with respect to the mean vector and the covariance matrix. They also addressed deprecated function calls and implemented a minimal test for the `inv_gamma` distribution to ensure samples are positive, contributing to improved code quality and reliability.
roboticsdifferentiable-programmingjulia-languagedeep-learningsystem-programming
ciemss/pyciemss
Jan 2023 - Feb 2025
Causal and probabilistic reasoning with continuous time dynamical systems
Contributions:10 releases, 116 reviews, 195 PRs in 2 years 1 month
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