Seth Flaxman
Associate Professor
Oxford, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolSeth Flaxman is an associate professor and machine learning researcher with 11 years of experience building scalable spatiotemporal and Bayesian models for public policy and the social sciences. He combines academic leadership at Oxford with deep technical contributions—such as performance-focused refactors to the widely used Stan Math C++ library—bridging theory and production-ready probabilistic tooling. Trained at Carnegie Mellon (PhD) and Harvard (BA), he brings rigorous mathematical foundations to practical problems in policy-relevant inference. Known for developing flexible models and scalable methods, he frequently works at the intersection of statistics, computation, and real-world impact.
11 years of coding experience
2 years of employment as a software developer
BA, Mathematics and Computer Science, BA, Mathematics and Computer Science at Harvard UniversityPhD, Machine learning and public policy, PhD, Machine learning and public policy at Carnegie Mellon University
French, ייִדיש
Github Skills (11)
mathematics10
mathlib10
automatic-differentiation10
c-language10
stan10
eigen10
cprogramming-language10
math10
math-library10
cpp8
boost8
Programming languages (7)
Github contributions (5)
stan-dev/math
Mar 2016 - Mar 2016
The Stan Math Library is a C++ template library for automatic differentiation of any order using forward, reverse, and mixed modes. It includes a range of built-in functions for probabilistic modeling, linear algebra, and equation solving.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:5 commits, 2 pushes in 1 day
Contributions summary:Seth primarily focused on refactoring and enhancing the Stan Math Library, a C++ library for automatic differentiation. Their contributions included removing a for loop to optimize the `cov_sq_exp.hpp` file and merging a feature branch related to squared exponential kernels. They also updated files like `distance.hpp` and `squared_distance.hpp` and modified test files to test implemented functionality. The user's work involved changes to the library's core mathematical functions and tests, demonstrating a focus on improving its functionality and performance.
automatic-differentiationprobabilistic-modelingsundialsmodestemplate-library
ImperialCollegeLondon/R0t
Mar 2020 - Mar 2020
Modeling R0(t) for covid-19
Contributions:281 pushes, 1 branch, 5 comments in 9 days
agent-based-modelingmachine-learningmodelingmodeling-toolvisualization
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