Christopher Musco
Description: Aarshvi Gajjar is a Ph.D. student at NYU
machine learning (3717) statistics (1447) computer science (1309) deep learning (1235) linear algebra (25) numerical linear algebra (7) statistical machine learning (5) dimensionality reduction (5) randomized algorithms (4) high dimensional statistics (3)
I am a third year Ph.D. student in the Algorithms and Foundations Group at NYU. My advisors are Christopher Musco and Chinmay Hegde .
I completed MS from UMass with Cameron Musco as my advisor. Prior to that, I worked as a Strat at Goldman Sachs and obtained my undergraduate degree from IIIT Hyderabad.
My research centers around algorithms for data-limited problems. Specifically, questions that interest me are: How many samples are required to approximately solve nonlinear regression? How does this requirement vary for different nonlinear function classes? I am also currently exploring how adding some noise to the Hessian impacts optimisation algorithms. In my research, I employ tools from theoretical computer science, high-dimensional statistics, and approximation theory.
Christopher Musco