Gabriel Mancino-ball
Lead Machine Learning Research Scientist at STR
Cambridge, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolGabriel Mancino-ball is a Lead Machine Learning Research Scientist based in Cambridge, MA, with a PhD in computational and applied mathematics and eight years of experience applying optimization theory to real-world ML and data problems. He builds production-ready ML systems and pipelines—most recently driving cash flow forecasting in Azure ML that cut MAPE from 55% to 15% and designing LLM-based consolidations that saved substantial labor costs. His research blends rigorous mathematical analysis with distributed and decentralized algorithm implementation, evidenced by peer-reviewed publications (AAAI, IEEE TSP) and internships developing decentralized GNN training. Comfortable across cloud, databases, and algorithm development, he has delivered large-scale performance wins (e.g., a custom BFS that sped TB-scale graph traversal 4x) and contributes to open-source Bayesian optimization tooling. Colleagues find him equally at home proving convergence theorems and shipping features in production—a rare mix that accelerates both research and impact.
8 years of coding experience
6 years of employment as a software developer
Bachelor's degree, Mathematics, Bachelor's degree, Mathematics at Winona State UniversityDoctor of Philosophy - PhD, Computational and Applied Mathematics, Doctor of Philosophy - PhD, Computational and Applied Mathematics at Rensselaer Polytechnic Institute
Github Skills (11)
bayesian10
cheminformatics10
experimental-design10
multi-objective-optimization10
active-learning10
optimization9
julia9
css8
react8
html7
javascript6
Programming languages (2)
Github contributions (5)
gmancino/old-website
Aug 2019 - Jun 2024
Contributions:3 pushes, 1 branch in 4 years 11 months
gmancino/gmancino.github.io
Jul 2018 - Aug 2021
This is my personal website
Contributions:285 commits, 3 PRs, 302 pushes in 3 years 1 month
cssjavascriptreact
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