I am a PhD student at University of Maryland advised by Prof. Hajiaghayi. Prior to it, I finished my undergraduate studies at Sharif University of Technology.

My research interest, broadly speaking, is in the design and analysis of algorithms. Particularly, I am interested in algorithms for processing large-scale graphs in parallel/distributed, streaming, dynamic, and stochastic settings. I am also interested in algorithmic game theory.

I am a recipient of Google PhD Fellowship 2019. Publications
Fully Dynamic Matching: Beating 2-Approximation in ∆ε Update Time
S. Behnezhad, J. Łącki, V. Mirrokni
The 31st Annual ACM-SIAM Symposium on Discrete Algorithms
For fully dynamic graphs, we have algorithms that maintain a 2-approximation of maximum matching extremely fast. But all known algorithms for general graphs maintaining a better-than-2 approximate matching require a large polynomial update time. In this paper, we show that in this regime, the update time can be improved to any arbitrarily small polynomial. Namely, we give an algorithm that for any desirably small constant ε>0, takes only O(∆ε) update-time.
Fully Dynamic Maximal Independent Set with Polylogarithmic Update Time
60th Annual IEEE Symposium on Foundations of Computer Science
This paper presents the first algorithm for maintaining a maximal independent set (and related problems) in fully dynamic graphs that takes polylogarithmic time per update. Previous algorithms required polynomial update time.
Exponentially Faster Massively Parallel Maximal Matching
S. Behnezhad, M. Hajiaghayi, D. Harris
60th Annual IEEE Symposium on Foundations of Computer Science
This paper presents exponentially faster Massively Parallel Computation (MPC) algorithms for maximal matching. This is achieved by providing an analysis of a natural algorithm which was conjectured to work by Czumaj et al. [STOC 2018]. The analysis reveals surprising connections between MPC and the notion of query-complexity developed for sublinear algorithms.
Near-Optimal Massively Parallel Graph Connectivity
60th Annual IEEE Symposium on Foundations of Computer Science
This paper presents a strongly sublinear space Massively Parallel Computation algorithm for graph connectivity which for graphs with diameter D > logεn, takes O(log D) rounds and takes O(log log n) rounds on other graphs. This improves upon an algorithm of Andoni et al. [FOCS 2019] and almost settles the problem due to an Ω(log D) conditional lower bound.
Streaming and Massively Parallel Algorithms for Edge Coloring
S. Behnezhad, M. Derakhshan, M. Hajiaghayi, M. Knittel, H. Saleh
27th Annual European Symposium on Algorithms
This paper initiates the study of edge coloring in the streaming model. In addition, an improved Massively Parallel Computation algorithm for edge coloring is presented.
Stochastic Matching on Uniformly Sparse Graphs
12th International Symposium on Algorithmic Game Theory
Massively Parallel Computation of Matching and MIS in Sparse Graphs
ACM Symposium on Principles of Distributed Computing
This paper presents strongly sublinear space Massively Parallel Computation algorithms for maximal matching and maximal independent set that for graphs with polylogarithmic arboricity (which includes most families of sparse graphs such as minor-free graphs and bounded-genus graphs) take O(log2 log n) rounds. Previous algorithms required polylogarithmic rounds.
Optimal Strategies of Blotto Games: Beyond Convexity
The 20th ACM Conference on Economics and Computation
This paper presents algorithms providing near-optimal strategies for the Colonel Blotto game that in addition have small support size. Previous algorithms produced strategies with large support. While bounding the support size makes the solution space non-convex (and thus prevents the use of convex programming), we show through a set of structural results that the space can be decomposed into polynomially many disjoint convex polytopes that can be solved independently.
Massively Parallel Computation via Remote Memory Access
The 31st ACM Symposium on Parallelism in Algorithms and Architectures
Invited to TOPC, special issue for SPAA'19.
This paper introduces the Adaptive Massively Parallel Computations (AMPC) model which is an extension of the Massively Parallel Computations (MPC) model. Compared to MPC, AMPC allows the machines to also adaptively query a read-only data store within a round, subject to the same communication bounds as in MPC. This feature is inspired by RDMA systems that are widely available. We give AMPC algorithms that are significantly faster than the state-of-the-art for MPC.
Stochastic Matching with Few Queries: New Algorithms and Tools
The 30th Annual ACM-SIAM Symposium on Discrete Algorithms
This paper presents improved non-adaptive algorithms for the stochastic matching problem. This is achieved by analysing a natural randomized algorithm that differs from those in the literature in a fundamental way. The improvement for unweighted graphs is significant: from the previous close-to-half approximations to 0.6568 approximation. For weighted graphs, the approximation factor is at least 0.501, which is the first to break half.
Almost Optimal Stochastic Weighted Matching With Few Queries
The 19th ACM Conference on Economics and Computation
This paper presents improved stochastic matching algorithms for weighted graphs. The main result is an adaptive algorithm that obtains a (1-ε) approximation using a constant number of queries per vertex. Previous algorithms for weighted graphs required super-constant queries.
Spatio-Temporal Games Beyond One Dimension
The 19th ACM Conference on Economics and Computation
This paper considers a generalization of spatio-temporal games from a one-dimensional space to graphs. An approximation algorithm (that relaxes the number of resources) is given for general graphs and a dependent-rounding technique is also shown to provide an algorithm for the one-dimensional case that simplifies the prior work.
Brief Announcement: MapReduce Algorithms on Massive Trees
The 45th International Colloquium on Automata, Languages, and Programming
Winning Strategies of Blotto and Auditing Games
The 29th Annual ACM-SIAM Symposium on Discrete Algorithms
This paper introduces a notion of (u, p)-maxmin strategies which guarantee receiving a minimum utility of u with probability at least p. It is shown that (u, p)-maximin strategies of the Colonel Blotto and auditing games can be well-approximated in polynomial time.
Affinity Clustering: Hierarchical Clustering at Scale
The 30th Annual Conference on Neural Information Processing Systems
This paper proposes affinity clustering which is a hierarchical clustering algorithm based on Boruvka's minimum spanning tree algorithm. It is shown to be superior to many other clustering methods on a number of public and private data sets. In addition, the paper presents fast large-scale algorithms for affinity clustering.
A Polynomial Time Algorithm for Spatio-Temporal Security Games
The 18th ACM Conference on Economics and Computation
This paper presents the first polynomial time algorithm for a well-studied variant of security games that is played out in space and time. Prior work obtained polynomial-time algorithms only for special cases of the problem e.g., when there are a constant number of timesteps.
Brief Announcement: Graph Matching in Massive Datasets
S. Behnezhad, M. Derakhshan, H. Esfandiari, E. Tan, H. Yami
The 29th ACM Symposium on Parallelism in Algorithms and Architectures
This paper presents a remarkably simple algorithm for approximate maximum matching of bipartite graphs in the streaming and Massively Parallel Computations models. The algorithm requires 1/ε rounds and O(n1.5) central space to achieve (1-ε) approximation.
Faster and Simpler Algorithm for Optimal Strategies of Blotto Game
The 31st AAAI Conference on Artificial Intelligence
This paper presents a linear programming (LP) formulation for the well-studied Colonel Blotto game with polynomially many constraints. Prior work relied on the ellipsoid method to solve an exponential size LP. It is further shown that the number of constraints of the formulation is optimal.
Visits/Internships
Research intern at Google Research, New York — Summer 2019
Visiting graduate student at the Simons Institute, UC Berkeley — Fall 2018
Research intern at Upwork, Mountain View — Summer 2018
Host: Nima Reyhani
Visiting graduate student at the Simons Institute, UC Berkeley — Spring 2018
Research intern at Upwork, Mountain View — Summer 2017
Host: Nima Reyhani
Contact
Brendan Iribe Center for Computer Science and Engineering
Room 5104
8125 Paint Branch Dr.
College Park, MD USA 20742

soheil@cs.umd.edu
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