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Hi, I am Tushar Vaidya.
I'm a researcher exploring quantum algorithms and their intersections with partial differential equations (PDEs), theoretical machine learning and algebra. Currently, I'm bridging insights from my past postdoc in random learning models with new projects in quantum computing.
I earned my PhD from the Singapore University of Technology and Design (SUTD) in August 2019, advised by Georgios Piliouras. My thesis, Social Learning Models: Deterministic and Probabilistic Aspects, developed novel discrete and continuous-time models from scratch. Key contributions included new central limit theorems for discrete random dynamical systems and asymptotic results for stochastic differential equations (SDEs) in social learning contexts. I drew on tools from stochastic calculus, dynamical systems, and optimal transport theory. During my PhD, I collaborated with leading experts across Europe, the US, and Asia. And became a proud father to two boys, now fueling my love for crafting action-packed stories on the fly!
Before SUTD, I studied at the University of Chicago, UC Berkeley, and Warwick University. Earlier in my career, I worked in fixed income derivatives as a quantitative trader and strategist. I still like problems from mathematical finance.
Good stories, like good research, are never linear. Let's connect and create some twists!
Research Fellow NTU 2022 - present I work with Milé Gu's quantum group. The transition to physics has had a phase change! I am also working with Patrick Pun. It's early days, but I have started work on some quantum algorithms in finance, taking a fresh look at derivatives pricing and portfolio optimization. Along the way, I learnt some quantum mechanics: Schrödingerizing PDEs. A textbook is also being prepared for mathematicians getting started in quantum algorithms.
Postdoctoral Fellow Temasek Labs - SUTD 2019 - 2022 I was a postdoc under Ernest Chong in ISTD. Our project aim was to bring together aspects of Algebraic Geometry and Commutative Algebra to machine reasoning and general AI. We have a working model of solving AI problems and the first paper appeared in CVPR 2023. Another paper, with a different team, was on using tropical algebra for adapter pruning in neural networks.
Research Interests: Derivatives, applied probability, game theory, and social networks. Generally, I like problems to come from practical situations and then think about the rigorous theory. My training is in mathematical finance. I prefer problems that have some randomness and connections to dynamical systems. Recently, I have started to look at computational algebraic geometry applied to artificial intelligence with probability always inherent in whatever I do. I am beginning to learn algebra but the process is slow. Still, one day I hope to be called an algebraic probabilist!
Thesis:
Collaborators and friends: Ionel Popescu, Ioannis Panageas, Niels Nygaard, Darren Rhea, Sai Ganesh Nagarajan, Mark Dragan, Thiparat Chotibut, Curt Hansen, Yuri Balasanov, Rishabh Bhardwaj, Ranjith Nair, Patrick (Chi Seng) Pun.
Contact me on LinkedIn if you prefer a more business-like environment. If you feel passionately about volatility smiles and multiagent systems, I would be happy to hear from you.
Service: I am a reviewer for AISTATS, Royal Society Open Science, WINE, Statistics and Computing, Transactions on Pattern Analysis and Machine Intelligence and Quantitative Finance. I am on the program committee for ACM ICAIF and AAAI.
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