World's Best Scientists 2026 revealed!
Ankur Moitra

Ankur Moitra

D-Index & Metrics

Mathematics

D-Index
43
Citations
6413
World Ranking
1719
National Ranking
740

Computer Science

D-Index
43
Citations
6484
World Ranking
8080
National Ranking
3465

Ankur Moitra publication distribution in Mathematics in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mathematics in 2026. The highlighted bar marks where Ankur Moitra sits on this spectrum.

42–46 publications: 3 scientists 47–51 publications: 5 scientists 52–56 publications: 7 scientists 57–61 publications: 20 scientists 62–66 publications: 14 scientists 67–71 publications: 25 scientists 72–76 publications: 19 scientists 77–81 publications: 35 scientists 82–86 publications: 50 scientists 87–91 publications: 60 scientists 92–96 publications: 86 scientists 97–101 publications: 84 scientists 102–106 publications: 83 scientists 107–111 publications: 90 scientists 112–116 publications: 99 scientists 117–121 publications: 90 scientists 122–126 publications: 91 scientists 127–131 publications: 109 scientists 132–136 publications: 110 scientists 137–141 publications: 98 scientists 142–146 publications: 112 scientists 147–151 publications: 102 scientists 152–156 publications: 88 scientists 157–161 publications: 106 scientists 162–166 publications: 83 scientists 167–171 publications: 102 scientists 172–176 publications: 77 scientists 177–181 publications: 81 scientists 182–186 publications: 78 scientists 187–191 publications: 71 scientists 192–196 publications: 92 scientists 197–201 publications: 64 scientists 202–206 publications: 69 scientists 207–211 publications: 64 scientists 212–216 publications: 62 scientists 217–221 publications: 58 scientists 222–226 publications: 53 scientists 227–231 publications: 50 scientists 232–236 publications: 46 scientists 237–241 publications: 46 scientists 242–246 publications: 46 scientists 247–251 publications: 43 scientists 252–256 publications: 29 scientists 257–261 publications: 45 scientists 262–266 publications: 30 scientists 267–271 publications: 33 scientists 272–276 publications: 34 scientists 277–281 publications: 30 scientists 282–286 publications: 31 scientists 287–291 publications: 21 scientists 292–296 publications: 34 scientists 297–301 publications: 26 scientists 302–306 publications: 10 scientists 307–311 publications: 17 scientists 312–316 publications: 23 scientists 317–321 publications: 13 scientists 322–326 publications: 16 scientists 327–331 publications: 26 scientists 332–336 publications: 13 scientists 337–341 publications: 13 scientists 342–346 publications: 16 scientists 347–351 publications: 17 scientists 352–356 publications: 12 scientists 357–361 publications: 18 scientists 362–366 publications: 18 scientists 367–371 publications: 9 scientists 372–376 publications: 11 scientists 377–381 publications: 8 scientists 382–386 publications: 8 scientists 387–391 publications: 9 scientists 392–396 publications: 9 scientists 397–401 publications: 8 scientists 402–406 publications: 11 scientists 407–411 publications: 6 scientists 412–416 publications: 6 scientists 417–421 publications: 9 scientists 422–426 publications: 8 scientists 427–431 publications: 5 scientists 432–436 publications: 8 scientists 437–441 publications: 8 scientists 442–446 publications: 4 scientists 447–451 publications: 4 scientists 452–456 publications: 4 scientists 457–461 publications: 2 scientists 462–466 publications: 2 scientists 467–471 publications: 4 scientists 472–476 publications: 3 scientists 477–481 publications: 3 scientists 482–486 publications: 6 scientists 487–491 publications: 3 scientists 492–496 publications: 5 scientists 497–501 publications: 5 scientists 502–506 publications: 1 scientists 507–511 publications: 6 scientists 512–516 publications: 4 scientists 517–521 publications: 1 scientists 522–526 publications: 3 scientists 527–531 publications: 1 scientists 532–536 publications: 4 scientists 537+ publications: 100 scientists
42 publications 537+

This scientist: 146 publications — 36th percentile

36% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 537 publications or more.

Ankur Moitra D-index placement in Mathematics in 2026

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

30 D-Index: 174 scientists 31 D-Index: 151 scientists 32 D-Index: 174 scientists 33 D-Index: 117 scientists 34 D-Index: 136 scientists 35 D-Index: 127 scientists 36 D-Index: 145 scientists 37 D-Index: 153 scientists 38 D-Index: 150 scientists 39 D-Index: 150 scientists 40 D-Index: 138 scientists 41 D-Index: 136 scientists 42 D-Index: 93 scientists 43 D-Index: 108 scientists 44 D-Index: 115 scientists 45 D-Index: 112 scientists 46 D-Index: 103 scientists 47 D-Index: 75 scientists 48 D-Index: 59 scientists 49 D-Index: 67 scientists 50 D-Index: 60 scientists 51 D-Index: 57 scientists 52 D-Index: 59 scientists 53 D-Index: 62 scientists 54 D-Index: 60 scientists 55 D-Index: 50 scientists 56 D-Index: 42 scientists 57 D-Index: 54 scientists 58 D-Index: 50 scientists 59 D-Index: 42 scientists 60 D-Index: 41 scientists 61 D-Index: 35 scientists 62 D-Index: 40 scientists 63 D-Index: 21 scientists 64 D-Index: 31 scientists 65 D-Index: 27 scientists 66 D-Index: 29 scientists 67 D-Index: 19 scientists 68 D-Index: 25 scientists 69 D-Index: 17 scientists 70 D-Index: 18 scientists 71 D-Index: 12 scientists 72 D-Index: 14 scientists 73 D-Index: 13 scientists 74 D-Index: 18 scientists 75 D-Index: 9 scientists 76 D-Index: 11 scientists 77 D-Index: 10 scientists 78 D-Index: 9 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 10 scientists 82 D-Index: 5 scientists 83 D-Index: 5 scientists 84 D-Index: 13 scientists 85 D-Index: 6 scientists 86+ D-Index: 99 scientists
30 D-Index 86+

This scientist: 43 D-Index — 54th percentile

54% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 86 D-Index or more.

Overview

Ankur Moitra is a researcher affiliated with MIT in the United States, specializing in computer science and mathematics. Their work spans multiple subfields, including artificial intelligence, statistics and probability, statistical and nonlinear physics, computational theory and mathematics, and computational mechanics.

Their research topics cover areas such as machine learning and algorithms, Bayesian modeling and causal inference, sparse and compressive sensing techniques, Markov chains and Monte Carlo methods, statistical methods and inference, tensor decomposition and applications, and reinforcement learning in robotics.

Recent papers authored or co-authored by Ankur Moitra include:

  • Noisy tensor completion via the sum-of-squares hierarchy, 2022, Mathematical Programming
  • Distilling Model Failures as Directions in Latent Space, 2022, arXiv (Cornell University)
  • Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Connections to Evolvability, 2020, arXiv (Cornell University)
  • Rigorous Guarantees for Tyler's M-estimator via quantum expansion, 2020, arXiv (Cornell University)
  • Algorithmic Foundations for the Diffraction Limit, 2020, arXiv (Cornell University)

Frequent co-authors collaborating with Moitra include Allen Liu, Noah Golowich, Dhruv Rohatgi, Ainesh Bakshi, and Alexander S. Wein.

Frequently chosen venues for publication encompass:

  • arXiv (Cornell University)
  • Mathematical Programming
  • Israel Journal of Mathematics
  • Communications of the ACM
  • 2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS)

Ankur Moitra has contributed substantially to the scientific literature with 74 publications in computer science and 31 in mathematics. Within these disciplines, the focus on artificial intelligence through 54 works and 19 on statistics and probability highlights the interdisciplinary nature of their research.

Best Publications

  • Learning Topic Models -- Going beyond SVD

    Sanjeev Arora;Rong Ge;Ankur Moitra

  • A Practical Algorithm for Topic Modeling with Provable Guarantees

    Sanjeev Arora;Rong Ge;Yonatan Halpern;David Mimno

  • Computing a nonnegative matrix factorization -- provably

    Sanjeev Arora;Rong Ge;Ravindran Kannan;Ankur Moitra

  • Settling the Polynomial Learnability of Mixtures of Gaussians

    Ankur Moitra;Gregory Valiant

  • Efficiently learning mixtures of two Gaussians

    Adam Tauman Kalai;Ankur Moitra;Gregory Valiant

  • New Algorithms for Learning Incoherent and Overcomplete Dictionaries

    Sanjeev Arora;Rong Ge;Ankur Moitra

  • Robust Estimators in High-Dimensions Without the Computational Intractability

    Ilias Diakonikolas;Gautam Kamath;Daniel Kane;Jerry Li

  • Robust Estimators in High Dimensions without the Computational Intractability

    Ilias Diakonikolas;Gautam Kamath;Daniel M. Kane;Jerry Li

  • Simple, Efficient, and Neural Algorithms for Sparse Coding

    Sanjeev Arora;Rong Ge;Tengyu Ma;Ankur Moitra

  • Being Robust (in High Dimensions) Can Be Practical

    Ilias Diakonikolas;Gautam Kamath;Daniel M. Kane;Jerry Li

  • Optimality and Sub-optimality of PCA I: Spiked Random Matrix Models

    Amelia Perry;Alexander S. Wein;Afonso S. Bandeira;Ankur Moitra

  • Super-resolution, Extremal Functions and the Condition Number of Vandermonde Matrices

    Ankur Moitra

  • Noisy Tensor Completion via the Sum-of-Squares Hierarchy

    Boaz Barak;Ankur Moitra

  • Some Results on Greedy Embeddings in Metric Spaces

    Tom Leighton;Ankur Moitra

  • A nearly tight sum-of-squares lower bound for the planted clique problem

    Boaz Barak;Samuel B. Hopkins;Jonathan A. Kelner;Pravesh K. Kothari

  • Smoothed analysis of tensor decompositions

    Aditya Bhaskara;Moses Charikar;Ankur Moitra;Aravindan Vijayaraghavan

  • Approximation Algorithms for Multicommodity-Type Problems with Guarantees Independent of the Graph Size

    Ankur Moitra

  • Robustly learning a gaussian: getting optimal error, efficiently

    Ilias Diakonikolas;Gautam Kamath;Daniel M. Kane;Jerry Li

  • Algorithms and Hardness for Robust Subspace Recovery

    Moritz Hardt;Ankur Moitra

  • Some Results on Greedy Embeddings in Metric Spaces

    A. Moitra;T. Leighton

  • Nonnegative Matrix Factorization

    Ankur Moitra

  • Robustly Learning a Gaussian: Getting Optimal Error, Efficiently

    Alistair Stewart;Ilias Diakonikolas;Gautam Chetan Kamath;Daniel M Kane

Frequent Co-Authors

Rong Ge
Rong Ge Duke University
Sanjeev Arora
Sanjeev Arora Princeton University
Ilias Diakonikolas
Ilias Diakonikolas University of Wisconsin–Madison
Boaz Barak
Boaz Barak Harvard University
Daniel M. Kane
Daniel M. Kane University of California, San Diego
Moses Charikar
Moses Charikar Stanford University
Adam Tauman Kalai
Adam Tauman Kalai Microsoft (United States)
Ryan O'Donnell
Ryan O'Donnell Carnegie Mellon University
Gregory Valiant
Gregory Valiant Stanford University

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