Alex Lew
PhD Student at Duke University
Cambridge, Massachusetts, United States
Summary
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Rockstar🎓
Top SchoolAlex Lew is a PhD student at MIT with 11 years of software engineering experience, combining rigorous academic training in mathematics and computer science from Yale with hands-on development of probabilistic programming systems. Based in Cambridge, MA, he has contributed core inference logic and distribution DSL features to the Gen.jl probabilistic programming repo, including refactors of Metropolis-Hastings operators and gradient-related bug fixes. His background spans teaching and mentoring—running ML summer school sessions at Duke and several years as a teacher—so he communicates complex ideas clearly to diverse audiences. Early industry experience includes internships and contract work where he shipped production software and learned practical engineering discipline. He brings a research-driven approach to building robust backend systems and is comfortable operating at the intersection of theory, numerical methods, and production code. A less obvious strength is his pattern of improving developer-facing abstractions (DSLs and operators), which amplifies long-term project productivity beyond single-feature contributions.
11 years of coding experience
2 years of employment as a software developer
Bachelor of Science (BS), Mathematics and Computer Science, Bachelor of Science (BS), Mathematics and Computer Science at Yale University
Stackoverflow
2,074reputation
134kreached
26answers
11questions
parsing
top-5%
go
top-5%
Github Skills (17)
probabilistic-programming10
machine-learning10
julialang10
julia10
parsing9
go9
data-structures8
data-structure8
algorithms8
algorithm8
clojure6
instance-variables6
python6
elm6
json6
Programming languages (12)
Github contributions (5)
probcomp/Gen.jl
Jan 2019 - Jan 2023
A general-purpose probabilistic programming system with programmable inference
Role in this project:
Back-end Developer
Back-end DeveloperContributions:12 reviews, 65 commits, 24 PRs in 4 years 1 month
Contributions summary:Alex made significant contributions focused on the core inference logic of the probabilistic programming system. The commits involved renaming and refactoring of Metropolis-Hastings (MH) operators, including removing and documenting them. The user also added and refined distribution DSL capabilities by implementing features such as `@dist` function definitions, and supporting operations such as `exp`, `log`, and `getindex`. Their work included bug fixes related to the handling of gradients and transformed distributions.
roboticsdifferentiable-programmingjulia-languagedeep-learningsystem-programming
probcomp/PClean
Sep 2019 - May 2022
A domain-specific probabilistic programming language for scalable Bayesian data cleaning
Contributions:16 commits, 1 push, 2 comments in 2 years 8 months
cleaningscalablebayesian-inferencedata-cleansingmachine-learning
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