Visiting PHD Student at University of California, Berkeley
Atlanta, Georgia, United States
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Summary
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Senior
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Top School
Andrew Rosemberg is a Machine Learning PhD candidate and visiting researcher who blends optimization, differentiable programming, and model-based reinforcement learning to tackle time-sensitive control problems in energy and robotics. With nine years of experience across national labs, industry research teams, and academic labs—including AI4OPT, Los Alamos, Invenia, and UC Berkeley—he focuses on fast optimization surrogates with feasibility guarantees for real-world systems. He contributes to open-source tooling in mathematical optimization (notably JuMP.jl), improving core modeling functionality and usability. Comfortable moving between theory and implementation, Andrew applies hardware-aware acceleration and data-driven methods to safety-critical control and renewable energy challenges.
9 years of coding experience
3 years of employment as a software developer
Diplôme d'ingénieur Ingénierie, Diplôme d'ingénieur Ingénierie at Centrale Méditerranée
Doctor of Philosophy - PhD Machine Learning and Optimization, Doctor of Philosophy - PhD Machine Learning and Optimization at Georgia Institute of Technology
Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)
Role in this project:
Back-end Developer
Contributions:2 reviews, 6 commits, 1 PR in 1 day
Contributions summary:Andrew primarily contributed to the JuMP (Julia Modeling Language) project by enhancing core functionality. They focused on implementing and refining the `haskey` method for the `AbstractModel` type, adding documentation, and fixing style and typos. These changes indicate a focus on improving the usability and correctness of the JuMP library's core features.
Contributions:1 review, 122 commits, 23 PRs in 1 year 8 months
portfolio-optimizationoptimization
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Andrew Rosemberg - Visiting PHD Student at University of California, Berkeley