معرفی
Sushant Sachdeva is an Associate Professor in the Department of Computer Science at the University of Toronto Mississauga, part of the Mathematical and Computational Sciences faculty. He is also a Faculty Affiliate at the Vector Institute. His research focuses on algorithms, optimization, and their connections to machine learning and statistics, with a particular emphasis on graph algorithms, numerical linear algebra, and efficient computational methods.
Prior to his current position, he held roles including Research Scientist at Google (2014–2017), Postdoctoral Associate at Yale University under Daniel Spielman (2013–2014), and a Simons Fellow at the Simons Institute, UC Berkeley (2013). He also served as a Visitor at the Institute for Advanced Study (2019).
He earned his Ph.D. in Computer Science from Princeton University (2008–2013), advised by Sanjeev Arora, and a B.Tech. in Computer Science and Engineering from IIT Bombay (2004–2008). His work has been recognized with prestigious awards, including the 2023 Sloan Research Fellowship and the Best Paper Award at FOCS 2022.
His research spans theoretical computer science, with contributions to fast algorithms for graph problems, optimization techniques in machine learning, and efficient linear system solvers. He has organized workshops such as 'Laplacian Paradigm 2.0' at FOCS 2018 and served on program committees for top conferences like FOCS and STOC.
Current and past students include Lawrence Li, Yibin Zhao, and Deeksha Adil. His publications address foundational challenges in algorithms and their applications to practical computational problems.
