ViseHub
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Shubham (@codeviser)
“Hey there! If you haven’t landed on this page by accident, chances are that you already met CodeViser! ViseHub is his honest effort in highlighting his academic, professional, and idiosyncratic journey thus far!”
Focal Interests & Aspirations
Statistical & Sequential Learning, Optimization & Control, Game Theory, Stochastic Decision Dynamics, Econometrics
Pursuing research in designing & explaining intelligent systems in sequential/game-theoretic settings, I wish to create interpretable, mathematically transparent, and robust knowledge systems of inference facilitating decisions. Strategic decision settings, entailing high-risk yet compelling explanability stemming from a melange of stochastic natural phenomena, particularly excite me. Predicting dynamics in uncertain games, economic interactions, and physical processes are prime examples.
Education
- Masters at ETH Zurich in CS (majors: Mathematics of ML, Applied Statistics), 2022-present
- Thesis at Yu Group, Dept. of Statistics, UC Berkeley and Dept. of Mathematics, ETH Zurich, 2024
- Professional student at Stanford AI 2020 & 2021
- Bachelors at IIT, Roorkee in Computer Science and Electrical (specialize in Control Theory), 2014-2018
Work
- Researcher at Automatic Control Laboratory, ETH Zurich, 2022-2024
- Market Data Researcher at quant firm, Tower Research Capital, 2018-2022
- Strategist for customer acquisition maximization at AmEX, 2017
NEWS!
- (Sept’24) I have started my Master’s Thesis on Understanding and modeling PDEs with Randomized Neural Networks, jointly supervised by Dr. Jakob Heiss, Yu Group, Dept. of Statistics, UC Berkeley, and Dr. Beatrice Acciaio, Dept. of Mathematics, ETH Zurich
- (Sept’24) My work on Combinatorial Optimization under Randomness promoting Social Fairness is accepted at NeuirIPS’24! Excieted to meet other acceptances at Vancouver, Dec’24!
- (Nov’22) I have joined Advanced Control Laboratory, ETH Zurich as a student researcher to work with Nicolas Lanzetti, Dr. Pasquale, and Dr. Dörfler on Controlling information diffusion in random combinatorial space via probability density modulation