Alex Atanasov
Quantitative Researcher
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolAlex Atanasov is a quantitative researcher who fuses theoretical physics, machine learning, and financial markets to build scalable, data-driven decision systems. He is currently applying advanced ML and statistical methods to equities arbitrage at The D. E. Shaw Group in New York, bringing high-dimensional ML insights from academia to production risk and return modeling. He earned a PhD in Theoretical Physics from Harvard, with research spanning deep neural networks, kernel methods, transformers, and data-driven methods for learning performance in high dimensions, published in top ML venues. His background also includes hands-on roles at Jane Street, Google, Perimeter Institute, Yale, and MITRE, reflecting a broad track record across academia, tech, and finance with a focus on machine learning in sequential data and time series. He has worked on Kalman filters, state-space models, and generative inference for noisy time series, and his GitHub bio emphasizes a fascination with scaling and universality, signaling a deep interest in robust, generalizable methods. Based in New York, he combines rigorous theory with practical engineering to turn complex datasets into reliable, auditable strategies.
10 years of coding experience
6 years of employment as a software developer
PhD, Theoretical Physics, 4.0, PhD, Theoretical Physics, 4.0 at Harvard UniversityMathematics (MS, BS), Physics (BS), 4.0 Mathematics, 3.97 Physics, 3.92 Overall, Mathematics (MS, BS), Physics (BS), 4.0 Mathematics, 3.97 Physics, 3.92 Overall at Yale UniversityHigh School Diploma, Concentration in Optics and Modern Physics, High School Diploma, Concentration in Optics and Modern Physics at Thomas Jefferson High School for Science and Technology
English, Bulgarian, Latin, Macedonian
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (21)
sparse10
pde9
alex9
grid9
scientific-machine-learning9
julia9
discretization8
automatic-differentiation8
recurrent-neural-networks8
numerical7
keras4
neural-networks4
tensorflow4
deep-learning3
css2
Programming languages (3)
Github contributions (5)
ABAtanasov/Sparse-Grids
May 2016 - Aug 2016
Contributions:53 commits, 51 pushes, 1 branch in 2 months
ABAtanasov/GalerkinSparseGrids.jl
Jul 2016 - Jan 2020
Sparse Grid Discretization with the Discontinuous Galerkin Method for solving PDEs
Contributions:1 release, 139 commits, 3 PRs in 3 years 6 months
solvingautomatic-differentiationmethodsparsepde
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