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Sepehr Assadi

Assistant Professor
Research Area: 
Theory of Computing
Sublinear Algorithms and Lower bounds
Combinatorial Optimization

My primary research interest is in theoretical foundations of big data analysis. This in particular includes sublinear algorithms and lower bounds in various models of computation for processing massive datasets such as streaming, distributed communication, and sublinear time algorithms. More broadly, I am also interested in communication complexity, online algorithms, and algorithmic game theory.

I have recently obtained my PhD from the department of Computer & Information Science at University of Pennsylvania, where I was fortunate to have Sanjeev Khanna as my advisor. My PhD thesis, "Combinatorial Optimization on Massive Datasets: Streaming, Distributed, and Massively Parallel Computation", focuses on several fundamental combinatorial optimization problems in graph theory and submodular optimization in modern models of computation for processing massive datasets. 

Awards & Distinctions: 
  • Best Paper Award at Symposium on Discrete Algorithms SODA 2019
  • Best Paper Award at Symposium on Parallelism in Algorithms and Architectures SPAA 2017.
  • Best Student Paper Award at Symposium on Principles of Database Systems PODS 2017.
  • Best Paper Award at Conference on Web and Internet Economics WINE 2015.