Sushant Sachdeva

Sushant Sachdeva is an Associate Professor in the Department of Computer Science at the University of Toronto. His research focuses on theoretical computer science, algorithms, optimization, and their connections to statistics and machine learning. Specific areas include the design of fast algorithms for graph problems, approximation algorithms, numerical linear algebra, and algorithmic challenges in machine learning. He is affiliated with the Vector Institute and has held positions at Google, Yale University, and the Simons Institute. Sachdeva's work has been recognized with awards such as the 2025 Infosys Award in Engineering and Computer Science, the 2023 Frontiers of Science Award, and the Sloan Research Fellowship.

Sushant Sachdeva is an Associate Professor at the University of Toronto and a faculty member at the Vector Institute. His research focuses on Algorithms, Optimization, and Theoretical Computer Science. He develops efficient algorithms for problems such as maximum flow, minimum-cost flow, and spectral graph theory. His work includes provable algorithms for isotonic regression, Lipschitz learning on graphs, and fast solvers for connection Laplacians. Sachdeva's research emphasizes theoretical guarantees and practical efficiency, often leveraging approximation theory and randomized methods. His contributions span both fundamental algorithm design and applications in machine learning and network analysis.