Matthew Staib
VP, Quantitative Research
Cambridge, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolMatthew Staib is a VP of Quantitative Research at Two Sigma with 11 years of experience applying advanced optimization techniques—convex, submodular, robust, and nonconvex—to machine learning problems. He progressed through roles at Two Sigma from intern to leadership and has research internship experience at Google, underpinned by a PhD in EECS from MIT and MS/BS degrees from Stanford. Known for bridging deep theoretical insight with production-grade quantitative systems, he focuses on turning complex optimization theory into practical models for trading and risk. Based in Cambridge, MA, he combines academic rigor with hands-on team leadership and a track record of scaling research into deployed strategies. An understated strength is his comfort navigating both pure math and engineering trade-offs, making him effective at translating novel algorithms into measurable business impact.
11 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Electrical Engineering and Computer Science, Doctor of Philosophy (Ph.D.) Electrical Engineering and Computer Science at Massachusetts Institute of TechnologyMS Electrical Engineering, MS Electrical Engineering at Stanford University
English, Chinese
Github Skills (7)
deep-learning8
machine-learning8
python8
tensorflow7
neural-network6
ml5
deep-neural-networks5
Programming languages (2)
Github contributions (5)
mstaib/stochastic-barycenter-code
Nov 2017 - Nov 2017
Contributions:8 commits, 1 comment, 1 issue in 10 days
mstaib/robust-budget-allocation-code
Jun 2017 - Jun 2017
Contributions:5 commits, 2 pushes in 8 days
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.