Mohammadhossein Bateni
MohammadHossein Bateni is a staff research scientist at Google, where he is a member of the NYC Algorithms and Optimization Team. He obtained his Ph.D. and M.A. in Computer Science from Princeton University in 2011 and 2008, respectively, after finishing his undergraduate studies with a B.Sc. in Computer Engineering at Sharif University of Technology in 2006.
Hossein is broadly interested in combinatorics and combinatorial optimization. His research focuses on approximation algorithms, distributed computing, and analysis of game-theoretic models.
Authored Publications
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Google
Sequential Attention for Feature Selection
Taisuke Yasuda
Lin Chen
Proceedings of the 11th International Conference on Learning Representations (2023)
Optimal Fully Dynamic k-Center Clustering for Adaptive and Oblivious Adversaries
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Monika Henzinger
Andreas Wiese
Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)
Tackling Provably Hard Representative Selection via Graph Neural Networks
Transactions on Machine Learning Research (2023)
SubMix: Learning to Mix Graph Sampling Heuristics
Josh Dillon
Johannes Gasteiger
2023
Data Sampling using Locality Sensitive Hashing for Large Scale Graph Learning
Mohamed Farghal
Animesh Nandi
Sarath Shekkizhar
2023
Coresets Meet EDCS: Algorithms for Matching and Vertex Cover on Massive Graphs
Aaron Bernstein
Cliff Stein
Sepehr Assadi
Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), SIAM (2019), pp. 1616-1635
Categorical Feature Compression via Submodular Optimization
Lin Chen
International Conference on Machine Learning (2019), pp. 515-523
Beating Approximation Factor 2 for Minimum k-way Cut in Planar and Minor-free Graphs
Alireza Farhadi
MohammadTaghi Hajiaghayi
Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), SIAM (2019), pp. 1055-1068
