World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
84
Citations
42925
World Ranking
826
National Ranking
449

Avrim Blum publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Avrim Blum sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 259 publications — 65th percentile

65% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Avrim Blum D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Avrim Blum sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 84 D-Index — 94th percentile

94% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Research.com Recognitions

  • 2007 - ACM Fellow For contributions to learning theory and algorithms.
  • 1994 - Fellow of Alfred P. Sloan Foundation

Overview

Avrim Blum is affiliated with the Toyota Technological Institute at Chicago in the United States. Their research spans primarily across computer science, with focused contributions in several subfields including artificial intelligence, management science and operations research, computational theory and mathematics, computer networks and communications, and computer vision and pattern recognition.

The main topics of their work include:

  • Machine Learning and Algorithms
  • Adversarial Robustness in Machine Learning
  • Complexity and Algorithms in Graphs
  • Algorithms and Data Compression
  • Optimization and Search Problems
  • Markov Chains and Monte Carlo Methods
  • Advanced Bandit Algorithms Research

Recent papers by Avrim Blum demonstrate a breadth of study in theory and practical aspects of algorithm design and fairness in machine learning. Notable publications include:

  • "Ignorance Is Almost Bliss: Near-Optimal Stochastic Matching with Few Queries" (2020) in Operations Research
  • "Recovering from Biased Data: Can Fairness Constraints Improve Accuracy?" (2020) published by Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • "Random Smoothing Might be Unable to Certify ℓ∞ Robustness for High-Dimensional Images" (2020) on arXiv (Cornell University)
  • "Advancing Subgroup Fairness via Sleeping Experts" (2020) also appearing in Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • "Technical perspective: Algorithm selection as a learning problem" (2020) in Communications of the ACM

Avrim Blum's frequent co-authors include John E. Hopcroft, Ravindran Kannan, Kevin Stangl, Saba Ahmadi, and Keziah Naggita. Collaboration with these researchers occurs regularly across numerous publications.

The scholar has also contributed to book literature, publishing "Foundations of Data Science" in 2020 through Cambridge University Press, a work that has accumulated significant citations.

Publication venues where their work often appears include:

  • arXiv (Cornell University)
  • Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • Operations Research
  • Communications of the ACM
  • Proceedings of the AAAI Conference on Artificial Intelligence

Recognition of their contributions includes being named an ACM Fellow in 2007 for work in learning theory and algorithms, as well as a Fellow of the Alfred P. Sloan Foundation in 1994.

Best Publications

  • Combining labeled and unlabeled data with co-training

    Avrim Blum;Tom Mitchell

  • Selection of relevant features and examples in machine learning

    Avrim L. Blum;Pat Langley

  • Fast planning through planning graph analysis

    Avrim L. Blum;Merrick L. Furst

  • Correlation clustering

    N. Bansal;A. Blum;S. Chawla

  • Learning from Labeled and Unlabeled Data using Graph Mincuts

    Avrim Blum;Shuchi Chawla

  • Practical privacy: the SuLQ framework

    Avrim Blum;Cynthia Dwork;Frank McSherry;Kobbi Nissim

  • Noise-tolerant learning, the parity problem, and the statistical query model

    Avrim Blum;Adam Kalai;Hal Wasserman

  • A learning theory approach to noninteractive database privacy

    Avrim Blum;Katrina Ligett;Aaron Roth

  • A learning theory approach to non-interactive database privacy

    Avrim Blum;Katrina Ligett;Aaron Roth

  • Training a 3-Node Neural Network is NP-Complete

    Avrim Blum;Ronald L. Rivest

  • Original Contribution: Training a 3-node neural network is NP-complete

    Avrim L. Blum;Ronald L. Rivest

  • Approximation Algorithms for Orienteering and Discounted-Reward TSP

    Avrim Blum;Shuchi Chawla;David R. Karger;Terran Lane

  • Cryptographic Primitives Based on Hard Learning Problems

    Avrim Blum;Merrick L. Furst;Michael J. Kearns;Richard J. Lipton

  • Linear approximation of shortest superstrings

    Avrim Blum;Tao Jiang;Ming Li;John Tromp

  • The minimum latency problem

    Avrim Blum;Prasad Chalasani;Don Coppersmith;Bill Pulleyblank

  • Clearing algorithms for barter exchange markets: enabling nationwide kidney exchanges

    David J. Abraham;Avrim Blum;Tuomas Sandholm

  • On-line Algorithms in Machine Learning

    Avrim Blum

  • Beating the hold-out: bounds for K-fold and progressive cross-validation

    Avrim Blum;Adam Kalai;John Langford

  • Empirical Support for Winnow and Weighted-MajorityAlgorithms: Results on a Calendar Scheduling Domain

    Avrim Blum

  • Co-Training and Expansion: Towards Bridging Theory and Practice

    Maria-florina Balcan;Avrim Blum;Ke Yang

Frequent Co-Authors

Maria-Florina Balcan
Maria-Florina Balcan Carnegie Mellon University
Yishay Mansour
Yishay Mansour Tel Aviv University
Santosh Vempala
Santosh Vempala Georgia Institute of Technology
Adam Tauman Kalai
Adam Tauman Kalai Microsoft (United States)
Ariel D. Procaccia
Ariel D. Procaccia Harvard University
John Langford
John Langford Microsoft (United States)
Prabhakar Raghavan
Prabhakar Raghavan Google (United States)
Nikhil Bansal
Nikhil Bansal University of Michigan–Ann Arbor
Tuomas Sandholm
Tuomas Sandholm Carnegie Mellon University
MohammadTaghi Hajiaghayi
MohammadTaghi Hajiaghayi University of Maryland, College Park

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