World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
48
Citations
12086
World Ranking
6087
National Ranking
2742

Adam Tauman Kalai 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 Adam Tauman Kalai 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: 132 publications — 19th percentile

19% of scientists in this discipline score the same or lower.

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

Adam Tauman Kalai 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 Adam Tauman Kalai 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: 48 D-Index — 58th percentile

58% 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

  • 2008 - Fellow of Alfred P. Sloan Foundation

Overview

Adam Tauman Kalai is affiliated with Microsoft in the United States. Their research contributions span multiple areas within computer science, with a significant focus on artificial intelligence and machine learning.

Their publication record includes 46 works in computer science, with 34 specifically focused on artificial intelligence. Other subfields include sociology and political science, electrical and electronic engineering, computer vision and pattern recognition, and computer science applications.

The research topics covered by Kalai include:

  • Topic Modeling
  • Machine Learning and Algorithms
  • Adversarial Robustness in Machine Learning
  • Machine Learning and Data Classification
  • Natural Language Processing Techniques
  • Ferroelectric and Negative Capacitance Devices
  • Ethics and Social Impacts of AI

Frequent collaborators in their work include Lester Mackey, Vikas Garg, Varun Kanade, Myra Cheng, and Maria De-Arteaga.

Kalai's work has been published predominantly in venues such as arXiv (Cornell University), with 23 publications, as well as contributions to the Leibniz-Zentrum für Informatik (Schloss Dagstuhl), Data Mining and Knowledge Discovery, and Games and Economic Behavior.

Recent selected papers include:

  • Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies (2022), arXiv (Cornell University)
  • Textbooks Are All You Need (2023), arXiv (Cornell University)
  • OpenAI o1 System Card (2024), arXiv (Cornell University)
  • Do Language Models Know When They're Hallucinating References? (2023), arXiv (Cornell University)
  • Language Models Can Teach Themselves to Program Better (2022), arXiv (Cornell University)

In recognition of their work, Kalai was named a Fellow of the Alfred P. Sloan Foundation in 2008.

Best Publications

  • Man is to computer programmer as woman is to homemaker? debiasing word embeddings

    Tolga Bolukbasi;Kai-Wei Chang;James Zou;Venkatesh Saligrama

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

    Avrim Blum;Adam Kalai;Hal Wasserman

  • Online convex optimization in the bandit setting: gradient descent without a gradient

    Abraham D. Flaxman;Adam Tauman Kalai;H. Brendan McMahan

  • Efficient algorithms for online decision problems

    Adam Kalai;Santosh Vempala

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

    Avrim Blum;Adam Kalai;John Langford

  • Agnostically Learning Halfspaces

    Adam Tauman Kalai;Adam R. Klivans;Yishay Mansour;Rocco A. Servedio

  • Trust-based recommendation systems: an axiomatic approach

    Reid Andersen;Christian Borgs;Jennifer Chayes;Uriel Feige

  • Logarithmic regret algorithms for online convex optimization

    Elad Hazan;Adam Kalai;Satyen Kale;Amit Agarwal

  • Universal portfolios with and without transaction costs

    Avrim Blum;Adam Kalai

  • Efficiently learning mixtures of two Gaussians

    Adam Tauman Kalai;Ankur Moitra;Gregory Valiant

  • Adaptively Learning the Crowd Kernel

    Omer Tamuz;Ce Liu;Serge Belongie;Ohad Shamir

  • Analysis of Perceptron-Based Active Learning

    Sanjoy Dasgupta;Adam Tauman Kalai;Claire Monteleoni

  • Efficient algorithms for universal portfolios

    Adam Kalai;Santosh Vempala

  • Playing Games with Approximation Algorithms

    Sham M. Kakade;Adam Tauman Kalai;Katrina Ligett

  • Adaptively Learning the Crowd Kernel

    Omer Tamuz;Omer Tamuz;Ce Liu;Ohad Shamir;Adam Kalai

  • Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression

    Sham M Kakade;Varun Kanade;Ohad Shamir;Adam Kalai

  • Decoupled classifiers for fair and efficient machine learning

    Cynthia Dwork;Nicole Immorlica;Adam Tauman Kalai;Mark D. M. Leiserson

  • Simulated Annealing for Convex Optimization

    Adam Tauman Kalai;Santosh Vempala

  • A Machine Learning Framework for Programming by Example

    Aditya Menon;Omer Tamuz;Sumit Gulwani;Butler Lampson

  • The myth of the Folk Theorem

    Christian Borgs;Jennifer T. Chayes;Nicole Immorlica;Adam Tauman Kalai

  • Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression

    Sham Kakade;Adam Tauman Kalai;Varun Kanade;Ohad Shamir

Frequent Co-Authors

Jennifer Chayes
Jennifer Chayes University of California, Berkeley
Christian Borgs
Christian Borgs University of California, Berkeley
Avrim Blum
Avrim Blum Toyota Technological Institute at Chicago
Nicole Immorlica
Nicole Immorlica Microsoft (United States)
Ehud Kalai
Ehud Kalai Northwestern University
Kai-Wei Chang
Kai-Wei Chang University of California, Los Angeles
Santosh Vempala
Santosh Vempala Georgia Institute of Technology
James Zou
James Zou Stanford University
Moshe Tennenholtz
Moshe Tennenholtz Technion – Israel Institute of Technology
Sumit Gulwani
Sumit Gulwani Microsoft (United States)

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Earning a Computer Science degree in the USA opens doors to a wide range of online programs and career pathways in related fields. Many students explore interdisciplinary areas to enhance their employability and career prospects.

For those interested in law and technology, the cheapest online criminal justice degree can offer foundational knowledge in cybersecurity, digital forensics, and related tech-driven legal careers. Likewise, computer science skills are highly valued in business, making an accounting degree an excellent complement for data-driven roles in finance and auditing.

Demand for data specialists continues to rise. Pursuing the cheapest masters in data science helps those with a computer science background to maximize their analytical expertise and career options in data analytics, AI, and machine learning.

Technology is also shaping the construction industry. Earning a construction management degree online enables graduates to lead digital transformation in project management and smart construction solutions.

Exploring these related degrees empowers students to diversify their skills and tap into high-demand sectors for a dynamic and rewarding career path.

Best Scientists Citing Adam Tauman Kalai

Trending Scientists

Recently Published Articles