World's Best Scientists 2026 revealed!

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Computer Science

D-Index
46
Citations
8643
World Ranking
6849
National Ranking
3008

Mathematics

D-Index
46
Citations
8615
World Ranking
1358
National Ranking
606

Overview

Maxim Sviridenko is affiliated with Yahoo in the United States and focuses on research primarily in the fields of Computer Science and Engineering. Their work includes contributions to several subfields such as Computational Mechanics, Artificial Intelligence, Computer Vision and Pattern Recognition, Biomedical Engineering, and Numerical Analysis.

The research topics Maxim Sviridenko has explored encompass Sparse and Compressive Sensing Techniques, Photoacoustic and Ultrasonic Imaging, Image and Signal Denoising Methods, Advanced Text Analysis Techniques, Image Retrieval and Classification Techniques, Topic Modeling, and Advanced Optimization Algorithms Research.

Maxim Sviridenko's recent publications are mainly disseminated through the arXiv platform of Cornell University. The following papers illustrate their recent scholarly output:

  • VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Search, 2021, arXiv (Cornell University)
  • Sparse Convex Optimization via Adaptively Regularized Hard Thresholding, 2020, arXiv (Cornell University)
  • Local Search Algorithms for Rank-Constrained Convex Optimization, 2021, arXiv (Cornell University)
  • Iterative Hard Thresholding with Adaptive Regularization: Sparser Solutions Without Sacrificing Runtime, 2022, arXiv (Cornell University)
  • Gradient Descent Converges Linearly for Logistic Regression on Separable Data, 2023, arXiv (Cornell University)

Coauthor collaboration is a notable aspect of Sviridenko's research activities. Frequent collaborators include:

  • Kyriakos Axiotis
  • Shaunak Mishra
  • Mikhail Kuznetsov
  • Gaurav Srivastava

All documented publications appear in arXiv, indicating an emphasis on preprint dissemination within open research communities.

Best Publications

  • A note on maximizing a submodular set function subject to a knapsack constraint

    Maxim Sviridenko

  • Pipage Rounding: A New Method of Constructing Algorithms with Proven Performance Guarantee

    Alexander A. Ageev;Maxim Sviridenko

  • Buffer Overflow Management in QoS Switches

    Alexander Kesselman;Zvi Lotker;Yishay Mansour;Boaz Patt-Shamir

  • The Santa Claus problem

    Nikhil Bansal;Maxim Sviridenko

  • Tight approximation algorithms for maximum general assignment problems

    Lisa Fleischer;Michel X. Goemans;Vahab S. Mirrokni;Maxim Sviridenko

  • Non-monotone submodular maximization under matroid and knapsack constraints

    Jon Lee;Vahab S. Mirrokni;Viswanath Nagarajan;Maxim Sviridenko

  • Approximation algorithms for asymmetric TSP by decomposing directed regular multigraphs

    Haim Kaplan;Moshe Lewenstein;Nira Shafrir;Maxim Sviridenko

  • The diameter of a long-range percolation graph

    Don Coppersmith;David Gamarnik;Maxim Sviridenko

  • Approximation schemes for minimizing average weighted completion time with release dates

    F. Afrati;E. Bampis;C. Chekuri;D. Karger

  • Dynamic placement for clustered web applications

    A. Karve;T. Kimbrel;G. Pacifici;M. Spreitzer

  • An Improved Approximation Algorithm for the Metric Uncapacitated Facility Location Problem

    Maxim Sviridenko

  • Submodular Maximization over Multiple Matroids via Generalized Exchange Properties

    Jon Lee;Maxim Sviridenko;Jan Vondrák

  • Non-monotone submodular maximization under matroid and knapsack constraints

    Jon Lee;Vahab Mirrokni;Viswanath Nagarjan;Maxim Sviridenko

  • Maximizing Nonmonotone Submodular Functions under Matroid or Knapsack Constraints

    Jon Lee;Vahab S. Mirrokni;Viswanath Nagarajan;Maxim Sviridenko

  • Optimal Approximation for Submodular and Supermodular Optimization with Bounded Curvature

    Maxim Sviridenko;Jan Vondrák;Justin Ward

  • Approximation Algorithms for Maximum Coverage and Max Cut with Given Sizes of Parts

    Alexander A. Ageev;Maxim Sviridenko

  • Bin Packing in Multiple Dimensions: Inapproximability Results and Approximation Schemes

    Nikhil Bansal;Jos R. Correa;Claire Kenyon;Maxim Sviridenko

  • Dynamic application placement under service and memory constraints

    Tracy Kimbrel;Malgorzata Steinder;Maxim Sviridenko;Asser Tantawi

  • A Constant Approximation Algorithm for the One-Warehouse Multiretailer Problem

    Retsef Levi;Robin Roundy;David Shmoys;Maxim Sviridenko

  • Supermodularity and Affine Policies in Dynamic Robust Optimization

    Dan Andrei Iancu;Mayank Sharma;Maxim Sviridenko

  • Tight Approximation Algorithms for Maximum Separable Assignment Problems

    Lisa Fleischer;Michel X. Goemans;Vahab S. Mirrokni;Maxim Sviridenko

Frequent Co-Authors

Nikhil Bansal
Nikhil Bansal University of Michigan–Ann Arbor
Baruch Schieber
Baruch Schieber New Jersey Institute of Technology
Maurice Queyranne
Maurice Queyranne University of British Columbia
Jon Lee
Jon Lee University of Michigan–Ann Arbor
Jan Vondrák
Jan Vondrák Stanford University
Klaus Jansen
Klaus Jansen Kiel University
Vahab Mirrokni
Vahab Mirrokni Google (United States)
Don Coppersmith
Don Coppersmith IBM (United States)
Andrea Lodi
Andrea Lodi Cornell University

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