I will be joining Rutgers University as an assistant professor in Fall 2019.
I am a postdoctoral researcher at the Theory of Computation group at Princeton University working with Mark Braverman.
I obtained my PhD from the department of Computer & Information Science at University of Pennsylvania
and was very fortunate to have Sanjeev Khanna as my advisor.
Prior to that, I got my B.Sc. degree in Computer Engineering from department of Computer Engineering at
Sharif University of Technology, Iran.
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, massively parallel computation, and sublinear time algorithms.
More broadly, I am also interested in communication complexity, online algorithms, and algorithmic game theory.
Program Committees: SODA 2020
Click on each title for a summary of the paper, drafts, presentation slides, etc.
- Exponentially Improved Truthful Combinatorial Auctions with Submodular Bidders FOCS 2019
- Massively Parallel Algorithms for Finding Well-Connected Components PODC 2019
- Distributed Weighted Matching via Randomized Composable Coresets ICML 2019
- When Algorithms for Maximal Independent Set and Maximal Matching Run in Sublinear Time ICALP 2019
- Distributed and Streaming Linear Programming in Low Dimensions PODS 2019
- Polynomial Pass Lower Bounds for Graph Streaming Algorithms STOC 2019
- A Simple Sublinear-Time Algorithm for Counting Arbitrary Subgraphs via Edge Sampling ITCS 2019
Sublinear Algorithms for (∆ + 1) Vertex Coloring
Best Paper Award at SODA'19
- Coresets Meet EDCS: Algorithms for Matching and Vertex Cover on Massive Graphs SODA 2019
- Fully Dynamic Maximal Independent Set with Sublinear in n Update Time SODA 2019
- Stochastic Submodular Cover with Limited Adaptivity SODA 2019
- Towards a Unified Theory of Sparsification for Matching Problems SOSA 2019
Combinatorial Optimization on Massive Datasets: Streaming, Distributed, and
Massively Parallel Computation PhD ThesisEATCS Distinguished Dissertation Award, 2019Rubinoff Dissertation Award for Best Computer Science PhD Thesis at University of Pennsylvania, 2019
- Fully Dynamic Maximal Independent Set with Sublinear Update Time STOC 2018
- Tight Bounds on the Round Complexity of the Distributed Maximum Coverage Problem SODA 2018
Randomized Composable Coresets for Matching and Vertex Cover
SPAA 2017 and HALG 2018
Invited to Highlights of Algorithms HALG 2018Invited to TOPC special issue for SPAA'17 papers
- Learning with Limited Rounds of Adaptivity: Coin Tossing, Multi-Armed Bandits, and Ranking from Pairwise Comparisons COLT 2017
Combinatorial Auctions Do Need Modest Interaction
Invited to TEAC special issue for EC'17 papers
- The Stochastic Matching Problem: Beating Half with a Non-Adaptive Algorithm EC 2017