Sepehr Assadi is a postdoctoral researcher at Princeton University. He received his PhD from University of Pennsylvania in 2018 under the supervision of Sanjeev Khanna.
Sepehr has a broad interest in theoretical computer science with an emphasis on theoretical foundations of big data analysis. Most of his work involves sublinear algorithms and lower bounds for combinatorial optimization, specifically streaming and sublinear-time algorithms and communication complexity lower bounds. His doctoral thesis, “Combinatorial Optimization on Massive Datasets: Streaming, Distributed, and Massively Parallel Computation”, primarily focused on providing a unified framework for solving various graph and submodular optimization problems across several canonical computational models for sublinear algorithms. His research in this area has received best paper awards of SODA 2019 and SPAA 2017 and best student paper award of PODS 2017.