World's Best Scientists 2026 revealed!
Alexander Rakhlin

Alexander Rakhlin

D-Index & Metrics

Engineering and Technology

D-Index
56
Citations
8512
World Ranking
2912
National Ranking
877

Alexander Rakhlin 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 Alexander Rakhlin 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: 154 publications — 29th percentile

29% of scientists in this discipline score the same or lower.

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

Alexander Rakhlin 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 Alexander Rakhlin 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: 56 D-Index — 72nd percentile

72% of scientists in this discipline score the same or lower.

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

Overview

Alexander Rakhlin is affiliated with MIT in the United States and has contributed extensively to the field of Computer Science, with a specialization in Artificial Intelligence, Management Science and Operations Research, Computer Vision and Pattern Recognition, Computational Mechanics, and Statistics and Probability.

Their recent publications include works spanning several venues and topics. Notable papers are: "Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles" (2020, arXiv, Cornell University), published twice with different citation counts; "ColocML: machine learning quantifies co-localization between mass spectrometry images" (2020, Bioinformatics); "DeepCycle reconstructs a cyclic cell cycle trajectory from unsegmented cell images using convolutional neural networks" (2020, Molecular Systems Biology); and "Learning nonlinear dynamical systems from a single trajectory" (2020, arXiv, Cornell University).

Their research focuses on topics such as Advanced Bandit Algorithms Research, Machine Learning and Algorithms, Data Stream Mining Techniques, Reinforcement Learning in Robotics, Stochastic Gradient Optimization Techniques, Sparse and Compressive Sensing Techniques, and Adversarial Robustness in Machine Learning.

Frequent co-authors include Dylan J. Foster, Adam Block, Jian Qian, Ayush Sekhari, and Zeyu Jia. Many of these collaborations reflect sustained partnerships over multiple publications.

Alexander Rakhlin has published primarily in the following venues: arXiv (Cornell University) with 46 publications, Bioinformatics, Molecular Systems Biology, and Acta Numerica. These venues highlight a combination of preprint archives and specialized journals in computational biology and numerical analysis.

  • Advanced Bandit Algorithms Research
  • Machine Learning and Algorithms
  • Data Stream Mining Techniques
  • Reinforcement Learning in Robotics
  • Stochastic Gradient Optimization Techniques
  • Sparse and Compressive Sensing Techniques
  • Adversarial Robustness in Machine Learning

  • Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles (2020, arXiv, Cornell University)
  • ColocML: machine learning quantifies co-localization between mass spectrometry images (2020, Bioinformatics)
  • DeepCycle reconstructs a cyclic cell cycle trajectory from unsegmented cell images using convolutional neural networks (2020, Molecular Systems Biology)
  • Learning nonlinear dynamical systems from a single trajectory (2020, arXiv, Cornell University)

  • Dylan J. Foster
  • Adam Block
  • Jian Qian
  • Ayush Sekhari
  • Zeyu Jia

  • arXiv (Cornell University)
  • Bioinformatics
  • Molecular Systems Biology
  • Acta Numerica

Best Publications

  • Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization

    Alexander Rakhlin;Ohad Shamir;Karthik Sridharan

  • Automatic Instrument Segmentation in Robot-Assisted Surgery using Deep Learning

    Alexey A. Shvets;Alexander Rakhlin;Alexandr A. Kalinin;Vladimir I. Iglovikov

  • Size-independent sample complexity of neural networks

    Noah Golowich;Alexander Rakhlin;Ohad Shamir

  • Competing in the dark: An efficient algorithm for bandit linear optimization

    Jacob D Abernethy;Elad Hazan;Alexander Rakhlin

  • Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis

    Alexander Rakhlin;Alexey Shvets;Vladimir Iglovikov;Alexandr A. Kalinin

  • Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis

    Maxim Raginsky;Alexander Rakhlin;Matus Telgarsky

  • Adaptive Online Gradient Descent

    Elad Hazan;Alexander Rakhlin;Peter L. Bartlett

  • Just interpolate: Kernel “Ridgeless” regression can generalize

    Tengyuan Liang;Alexander Rakhlin

  • Online Learning With Predictable Sequences

    Alexander Rakhlin;Karthik Sridharan

  • Online Optimization : Competing with Dynamic Comparators

    Ali Jadbabaie;Alexander Rakhlin;Shahin Shahrampour;Karthik Sridharan

  • Paediatric Bone Age Assessment Using Deep Convolutional Neural Networks

    Vladimir I. Iglovikov;Alexander Rakhlin;Alexandr A. Kalinin;Alexey A. Shvets

  • Stochastic Convex Optimization with Bandit Feedback

    Alekh Agarwal;Dean P. Foster;Daniel J. Hsu;Sham M. Kakade

  • Optimal Strategies and Minimax Lower Bounds for Online Convex Games

    Jacob Duncan Abernethy;Peter Bartlett;Alexander Rakhlin;Ambuj Tewari

  • Deep learning: a statistical viewpoint

    Peter L. Bartlett;Andrea Montanari;Alexander Rakhlin

  • Fisher-Rao Metric, Geometry, and Complexity of Neural Networks

    Tengyuan Liang;Tomaso A. Poggio;Alexander Rakhlin;James Stokes

  • Does data interpolation contradict statistical optimality

    Mikhail Belkin;Alexander Rakhlin;Alexandre B. Tsybakov

  • Stability of K-Means Clustering

    Alexander Rakhlin;Andrea Caponnetto

  • Partial Monitoring—Classification, Regret Bounds, and Algorithms

    Gábor Bartók;Dean P. Foster;Dávid Pál;Alexander Rakhlin

  • Online Learning: Random Averages, Combinatorial Parameters, and Learnability

    Alexander Rakhlin;Karthik Sridharan;Ambuj Tewari

  • Distributed Detection: Finite-Time Analysis and Impact of Network Topology

    Shahin Shahrampour;Alexander Rakhlin;Ali Jadbabaie

  • Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles

    Dylan Foster;Alexander Rakhlin

Frequent Co-Authors

Karthik Sridharan
Karthik Sridharan Cornell University
Ambuj Tewari
Ambuj Tewari University of Michigan–Ann Arbor
Peter L. Bartlett
Peter L. Bartlett University of California, Berkeley
Tony Cai
Tony Cai University of Pennsylvania
Dean P. Foster
Dean P. Foster Amazon (United States)
Ohad Shamir
Ohad Shamir Weizmann Institute of Science
Alekh Agarwal
Alekh Agarwal Google (United States)

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