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I am a research associate at NYU/UChicago, working with Elena Manresa and Guillaume Pouliot at the intersection of simulation optimisation and econometrics. 

Previously I received a masters degree in Electrical and Computer Engineering from McGill University/Mila under the supervision of Doina Precup and Jeremy Cooperstock. I also collaborated with Aditya Mahajan and Prashanth L.A.

Broadly, my research focuses on theoretical aspects of Reinforcement Learning, Stochastic Approximation, and Probabilistic Inference. I am interested in using tools from algorithmic statistics, optimisation, and economics to design and analyse data-driven decision-making algorithms. I am also interested in investigating how the theoretical underpinnings of these algorithms affect their applicability to solve practical problems.