Mohammadhossein Bateni
MohammadHossein Bateni is a principal research scientist at Google, where he is a director 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 machine learning, in particular, on methods and applications for data selection.
Authored Publications
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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)
Data Sampling using Locality Sensitive Hashing for Large Scale Graph Learning
Mohamed Farghal
Animesh Nandi
Sarath Shekkizhar
2023
Sequential Attention for Feature Selection
Taisuke Yasuda
Lin Chen
Proceedings of the 11th International Conference on Learning Representations (2023)
SubMix: Learning to Mix Graph Sampling Heuristics
Josh Dillon
Johannes Gasteiger
2023
Cache-aware load balancing of data center applications
Aaron Schild
Ray Yang
Richard Zhuang
Proceedings of the VLDB Endowment, 12 (2019), pp. 709-723
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
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
Afshin Rostamizadeh
Lin Chen
International Conference on Machine Learning (2019), pp. 515-523
