World's Best Scientists 2026 revealed!
Sergei Vassilvitskii

Sergei Vassilvitskii

D-Index & Metrics

Engineering and Technology

D-Index
42
Citations
18145
World Ranking
6354
National Ranking
1740

Sergei Vassilvitskii publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Sergei Vassilvitskii sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 148 publications — 26th percentile

26% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Sergei Vassilvitskii D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Sergei Vassilvitskii sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 42 D-Index — 35th percentile

35% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

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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