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Sergei Vassilvitskii

Sergei Vassilvitskii

D-Index & Metrics

Engineering and Technology

D-Index
42
Citations
18145
World Ranking
6352
National Ranking
1739

Overview

Sergei Vassilvitskii is affiliated with Google in the United States and has a primary research focus in the field of Computer Science. Their body of work encompasses significant contributions across several subfields, including Artificial Intelligence, Computer Networks and Communications, Management Science and Operations Research, Computational Theory and Mathematics, and Sociology and Political Science.

The research topics addressed in their publications cover various areas, notably Privacy-Preserving Technologies in Data, Cryptography and Data Security, Optimization and Search Problems, Complexity and Algorithms in Graphs, Advanced Bandit Algorithms Research, Privacy, Security, and Data Protection, as well as Machine Learning and Algorithms.

Among the recent papers associated with their work are:

  • How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy (2023, Journal of Artificial Intelligence Research)
  • Algorithms with predictions (2022, Communications of the ACM)
  • Competitive Caching with Machine Learned Advice (2021, Journal of the ACM)
  • Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment (2024, Harvard Data Science Review)
  • Matroids, Matchings and Fairness (2021, IRIS Research product catalog, Sapienza University of Rome)

Vassilvitskii frequently collaborates with several colleagues, including Silvio Lattanzi, Alessandro Epasto, Benjamin Moseley, Vahab Mirrokni, and Michael Dinitz, with multiple joint publications reflecting ongoing partnerships in research.

Their scholarly contributions have appeared significantly in venues such as arXiv (Cornell University), where they have published extensively, as well as in journals like the Journal of Artificial Intelligence Research, Communications of the ACM, Harvard Data Science Review, and Journal of the ACM.

Best Publications

  • k-means++: the advantages of careful seeding

    David Arthur;Sergei Vassilvitskii

  • Scalable k-means++

    Bahman Bahmani;Benjamin Moseley;Andrea Vattani;Ravi Kumar

  • A model of computation for MapReduce

    Howard Karloff;Siddharth Suri;Sergei Vassilvitskii

  • How slow is the k-means method?

    David Arthur;Sergei Vassilvitskii

  • Counting triangles and the curse of the last reducer

    Siddharth Suri;Sergei Vassilvitskii

  • Fast Greedy Algorithms in MapReduce and Streaming

    Ravi Kumar;Benjamin Moseley;Sergei Vassilvitskii;Andrea Vattani

  • Generalized distances between rankings

    Ravi Kumar;Sergei Vassilvitskii

  • Filtering: a method for solving graph problems in MapReduce

    Silvio Lattanzi;Benjamin Moseley;Siddharth Suri;Sergei Vassilvitskii

  • Densest subgraph in streaming and MapReduce

    Bahman Bahmani;Ravi Kumar;Sergei Vassilvitskii

  • Fair Clustering Through Fairlets

    Flavio Chierichetti;Ravi Kumar;Silvio Lattanzi;Sergei Vassilvitskii

  • Competitive Caching with Machine Learned Advice

    Thodoris Lykouris;Sergei Vassilvitskii

  • A complete, local and parallel reconfiguration algorithm for cube style modular robots

    S. Vassilvitskii;M. Yim;J. Suh

  • Bidding for Representative Allocations for Display Advertising

    Arpita Ghosh;Preston Mcafee;Kishore Papineni;Sergei Vassilvitskii

  • How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy

    Unknown

  • Efficiently computing succinct trade-off curves

    Sergei Vassilvitskii;Mihalis Yannakakis

  • Local search methods for k-means with outliers

    Shalmoli Gupta;Ravi Kumar;Kefu Lu;Benjamin Moseley

  • Worst-Case and Smoothed Analysis of the ICP Algorithm, with an Application to the k-Means Method

    David Arthur;Sergei Vassilvitskii

  • Indexing Boolean expressions

    Steven Euijong Whang;Hector Garcia-Molina;Chad Brower;Jayavel Shanmugasundaram

  • Shuffles and Circuits (On Lower Bounds for Modern Parallel Computation)

    Tim Roughgarden;Sergei Vassilvitskii;Joshua R. Wang

  • The dynamics of repeat consumption

    Ashton Anderson;Ravi Kumar;Andrew Tomkins;Sergei Vassilvitskii

  • Optimal online assignment with forecasts

    Erik Vee;Sergei Vassilvitskii;Jayavel Shanmugasundaram

  • Online scheduling via learned weights

    Silvio Lattanzi;Thomas Lavastida;Benjamin Moseley;Sergei Vassilvitskii

Frequent Co-Authors

Ravi Kumar
Ravi Kumar Google (United States)
Mohammad Mahdian
Mohammad Mahdian Google (United States)
Jayavel Shanmugasundaram
Jayavel Shanmugasundaram Yahoo (United States)
Andrew Tomkins
Andrew Tomkins Google (United States)
Andrei Z. Broder
Andrei Z. Broder Google (United States)
R. Preston McAfee
R. Preston McAfee Google (United States)
Michael Mitzenmacher
Michael Mitzenmacher Harvard University
Sharad Goel
Sharad Goel Harvard University

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