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Citations
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130

Computer Science

D-Index
31
Citations
4896
World Ranking
13536
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5404

Aryan Mokhtari 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 Aryan Mokhtari 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: 164 publications — 32nd percentile

32% of scientists in this discipline score the same or lower.

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

Aryan Mokhtari 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 Aryan Mokhtari 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: 31 D-Index — 6th percentile

6% 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

  • 2025 - Research.com Rising Stars Award

Overview

Aryan Mokhtari is affiliated with The University of Texas at Austin in the United States. Their research primarily spans the field of computer science, with a focus on artificial intelligence, numerical analysis, computational mechanics, computational theory and mathematics, and computer networks and communications.

The scientist has contributed extensively to various research topics, including:

  • Stochastic Gradient Optimization Techniques
  • Sparse and Compressive Sensing Techniques
  • Advanced Optimization Algorithms Research
  • Privacy-Preserving Technologies in Data
  • Domain Adaptation and Few-Shot Learning
  • Iterative Methods for Nonlinear Equations
  • Machine Learning and Data Classification

They have authored numerous papers, with recent publications including:

  • Personalized Federated Learning: A Meta-Learning Approach (2020, arXiv (Cornell University))
  • Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity (2022, IEEE Journal on Selected Areas in Information Theory)
  • Exploiting Shared Representations for Personalized Federated Learning (2021, arXiv (Cornell University))
  • FedAvg with Fine Tuning: Local Updates Lead to Representation Learning (2022, arXiv (Cornell University))
  • Federated Learning with Compression: Unified Analysis and Sharp Guarantees (2020, arXiv (Cornell University))

Mokhtari has collaborated with several frequent coauthors, such as Ruichen Jiang, Hamed Hassani, Sanjay Shakkottai, Qiujiang Jin, and Liam Collins.

Their work appears regularly in notable publication venues, including:

  • arXiv (Cornell University)
  • SIAM Journal on Optimization
  • Mathematical Programming
  • IEEE Journal on Selected Areas in Information Theory
  • Proceedings of the IEEE

In addition to journal articles, Aryan Mokhtari has contributed to book publications. They have a forthcoming book titled Conditional Gradient Methods to be published by the Society for Industrial and Applied Mathematics in 2025.

Best Publications

  • Personalized Federated Learning: A Meta-Learning Approach

    Alireza Fallah;Aryan Mokhtari;Asuman E. Ozdaglar

  • Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach

    Alireza Fallah;Aryan Mokhtari;Asuman E. Ozdaglar

  • FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization.

    Amirhossein Reisizadeh;Aryan Mokhtari;Hamed Hassani;Ali Jadbabaie

  • FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization

    Amirhossein Reisizadeh;Aryan Mokhtari;Hamed Hassani;Ali Jadbabaie

  • RES: Regularized Stochastic BFGS Algorithm

    Aryan Mokhtari;Alejandro Ribeiro

  • Network Newton Distributed Optimization Methods

    Aryan Mokhtari;Qing Ling;Alejandro Ribeiro

  • Online optimization in dynamic environments: Improved regret rates for strongly convex problems

    Aryan Mokhtari;Shahin Shahrampour;Ali Jadbabaie;Alejandro Ribeiro

  • Global convergence of online limited memory BFGS

    Aryan Mokhtari;Alejandro Ribeiro

  • DSA: decentralized double stochastic averaging gradient algorithm

    Aryan Mokhtari;Alejandro Ribeiro

  • A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach.

    Aryan Mokhtari;Asuman E. Ozdaglar;Sarath Pattathil

  • A Class of Prediction-Correction Methods for Time-Varying Convex Optimization

    Andrea Simonetto;Aryan Mokhtari;Alec Koppel;Geert Leus

  • DQM: Decentralized Quadratically Approximated Alternating Direction Method of Multipliers

    Aryan Mokhtari;Wei Shi;Qing Ling;Alejandro Ribeiro

  • An Exact Quantized Decentralized Gradient Descent Algorithm

    Amirhossein Reisizadeh;Aryan Mokhtari;Hamed Hassani;Ramtin Pedarsani

  • Decentralized Quasi-Newton Methods

    Mark Eisen;Aryan Mokhtari;Alejandro Ribeiro

  • A Decentralized Second-Order Method with Exact Linear Convergence Rate for Consensus Optimization

    Aryan Mokhtari;Wei Shi;Qing Ling;Alejandro Ribeiro

  • On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms

    Alireza Fallah;Aryan Mokhtari;Asuman E. Ozdaglar

  • Decentralized quadratically approximated alternating direction method of multipliers

    Aryan Mokhtari;Wei Shi;Qing Ling;Alejandro Ribeiro

  • Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity.

    Amirhossein Reisizadeh;Isidoros Tziotis;Hamed Hassani;Aryan Mokhtari

  • FedAvg with Fine Tuning: Local Updates Lead to Representation Learning

    Unknown

  • Decentralized Prediction-Correction Methods for Networked Time-Varying Convex Optimization

    Andrea Simonetto;Alec Koppel;Aryan Mokhtari;Geert Leus

  • A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach

    Aryan Mokhtari;Asuman Ozdaglar;Sarath Pattathil

  • Direct runge-kutta discretization achieves acceleration

    Jingzhao Zhang;Aryan Mokhtari;Suvrit Sra;Ali Jadbabaie

  • Exploiting Shared Representations for Personalized Federated Learning

    Liam Collins;Hamed Hassani;Aryan Mokhtari;Sanjay Shakkottai

  • Conditional Gradient Method for Stochastic Submodular Maximization: Closing the Gap

    Aryan Mokhtari;S. Hamed Hassani;Amin Karbasi

Frequent Co-Authors

Alejandro Ribeiro
Alejandro Ribeiro University of Pennsylvania
Qing Ling
Qing Ling Sun Yat-sen University
Sanjay Shakkottai
Sanjay Shakkottai The University of Texas at Austin
Geert Leus
Geert Leus Delft University of Technology
Xin Wang
Xin Wang Fudan University
Georgios B. Giannakis
Georgios B. Giannakis University of Minnesota
Peilin Zhao
Peilin Zhao Tencent (China)

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