Jonathan Tuck
Gardening at Citadel
Chicago, Illinois, United States
Summary
🤩
Rockstar🎓
Top SchoolJonathan Tuck is a quantitative researcher and PhD candidate in electrical engineering at Stanford with nine years of experience applying optimization and machine learning to portfolio construction and quantitative finance. He has progressed from internships at Apple, Microsoft, and Cubist to multi-year research and quant roles at Citadel, where he focused on portfolio optimization within Global Quantitative Strategies. Jonathan blends rigorous academic research—developing accelerated bi-level optimization algorithms and subarray beamforming methods—with practical production problems in high-frequency finance. Known for adaptability and leading cross-disciplinary teams, he’s comfortable juggling multiple projects and translating theoretical insights into deployable solutions. An early robotics and hardware researcher, he still brings a hardware-aware, simulation-first mindset to algorithm design, which helps him bridge theory and implementation. Based in Chicago, he pairs high energy and a track record of awards and publications with hands-on expertise in math-heavy modeling and optimization.
9 years of coding experience
5 years of employment as a software developer
Bachelor of Science (B.S.) Electrical and Computer Engineering, Bachelor of Science (B.S.) Electrical and Computer Engineering at Georgia Tech LorraineHigh School, High School at The Weber SchoolStanford Ignite Fellow, Stanford Ignite Fellow at Stanford University Graduate School of BusinessBachelor of Science (B.S.) Electrical and Computer Engineering, Bachelor of Science (B.S.) Electrical and Computer Engineering at Georgia Institute of TechnologyDoctor of Philosophy (Ph.D.) Electrical Engineering, Doctor of Philosophy (Ph.D.) Electrical Engineering at Stanford University
Github Skills (11)
laplacian10
optimization9
python9
numerical-optimization9
convex-optimization9
mathematical-optimization9
cvxpy8
julia2
gameboy2
bare-metal1
raspberry-pi1
Programming languages (5)
Github contributions (5)
cvxgrp/strat_models
May 2019 - Mar 2021
A distributed method for fitting Laplacian regularized stratified models.
Contributions:49 commits, 51 pushes, 2 branches in 1 year 9 months
fittinglaplacianmethoddistributed
cvxgrp/mm_dist_lapl
Mar 2018 - May 2019
Contributions:16 commits, 3 PRs, 3 pushes in 1 year 1 month
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