Before starting his PhD at Stanford University as a Hertz Fellow, Aman Sinha spent a year at the University of Cambridge, where he combined techniques from control theory and machine learning towards building robust and scalable automated prediction systems. Now at Stanford, he approaches this problem as well as other “big-data” problems through the lens of optimization. In particular, he works on extending the theory of convex optimization to very large-scale systems, with applications including the control of epidemics in networks and the online optimization of regression models that are robust to underlying data distributions.
Aman’s interest in engineering began at age seven when he attempted to invent a novel clothes dryer that almost burnt down his house. As a mechanical and aerospace engineering major at Princeton University, he combined his penchant for mechanics with an ever-increasing interest in scientific computation, and its role in the analysis and control of dynamical systems. His senior thesis studied distributed consensus protocols in networks using techniques inspired by models of biological group behavior, such as flock migrations and bee swarms.
