World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
49
Citations
13816
World Ranking
5779
National Ranking
2628

Aram Galstyan 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 Aram Galstyan 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: 248 publications — 62nd percentile

62% of scientists in this discipline score the same or lower.

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

Aram Galstyan 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 Aram Galstyan 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: 49 D-Index — 60th percentile

60% of scientists in this discipline score the same or lower.

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

Overview

Aram Galstyan is affiliated with the University of Southern California in the United States. Their research primarily engages with the field of Computer Science, encompassing 183 publications with a focus on multiple subfields such as Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology, Statistics and Probability, and Statistical and Nonlinear Physics.

The scientist's work covers a diverse range of topics within these areas, including:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Domain Adaptation and Few-Shot Learning
  • Adversarial Robustness in Machine Learning
  • Advanced Graph Neural Networks
  • Multimodal Machine Learning Applications
  • Speech and Dialogue Systems

Galstyan's recent papers reflect active involvement in several research directions. Notable publications include:

  • "A Survey on Bias and Fairness in Machine Learning" (2021), published in ACM Computing Surveys
  • "Stacking models for nearly optimal link prediction in complex networks" (2020), published in Proceedings of the National Academy of Sciences
  • "Identifying and Analyzing Cryptocurrency Manipulations in Social Media" (2021), published in IEEE Transactions on Computational Social Systems
  • "Partner-Assisted Learning for Few-Shot Image Classification" (2021), presented at the 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • "A survey of human judgement and quantitative forecasting methods" (2021), published in Royal Society Open Science

Frequent coauthors contributing to these research efforts include Greg Ver Steeg, Ninareh Mehrabi, Kai-Wei Chang, Fred Morstatter, and Jwala Dhamala. Collaboration with these researchers spans multiple projects and publications.

Publication venues where Galstyan frequently contributes comprise:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Proceedings of the International AAAI Conference on Web and Social Media
  • ACM Computing Surveys
  • Proceedings of the National Academy of Sciences

Best Publications

  • A Survey on Bias and Fairness in Machine Learning

    Ninareh Mehrabi;Fred Morstatter;Nripsuta Saxena;Kristina Lerman

  • Multitask learning and benchmarking with clinical time series data.

    Hrayr Harutyunyan;Hrant Khachatrian;David C. Kale;Greg Ver Steeg

  • The DARPA Twitter Bot Challenge

    V.S. Subrahmanian;Amos Azaria;Skylar Durst;Vadim Kagan

  • The DARPA Twitter Bot Challenge

    V.S. Subrahmanian;Amos Azaria;Skylar Durst;Vadim Kagan

  • A review of probabilistic macroscopic models for swarm robotic systems

    Kristina Lerman;Alcherio Martinoli;Aram Galstyan

  • Distributed online localization in sensor networks using a moving target

    Aram Galstyan;Bhaskar Krishnamachari;Kristina Lerman;Sundeep Pattem

  • Analysis of Dynamic Task Allocation in Multi-Robot Systems

    Kristina Lerman;Chris Jones;Aram Galstyan;Maja J Mataríc

  • MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing

    Sami Abu-El-Haija;Bryan Perozzi;Amol Kapoor;Nazanin Alipourfard

  • Mathematical Model of Foraging in a Group of Robots: Effect of Interference

    Kristina Lerman;Aram Galstyan

  • Scalable Temporal Latent Space Inference for Link Prediction in Dynamic Social Networks

    Linhong Zhu;Dong Guo;Junming Yin;Greg Ver Steeg

  • Hormone-Inspired Self-Organization and Distributed Control of Robotic Swarms

    Wei-Min Shen;Peter Will;Aram Galstyan;Cheng-Ming Chuong

  • MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing

    Sami Abu-El-Haija;Bryan Perozzi;Amol Kapoor;Hrayr Harutyunyan

  • Information transfer in social media

    Greg Ver Steeg;Aram Galstyan

  • Predicting Online Extremism, Content Adopters, and Interaction Reciprocity

    Emilio Ferrara;Wen-Qiang Wang;Onur Varol;Alessandro Flammini

  • A macroscopic analytical model of collaboration in distributed robotic systems

    Kristina Lerman;Aram Galstyan;Alcherio Martinoli;Auke Ijspeert

  • Efficient Estimation of Mutual Information for Strongly Dependent Variables

    Shuyang Gao;Greg Ver Steeg;Aram Galstyan

  • Top-down vs bottom-up methodologies in multi-agent system design

    Valentino Crespi;Aram Galstyan;Kristina Lerman

  • Resource Allocation in the Grid Using Reinforcement Learning

    Aram Galstyan;Karl Czajkowski;Kristina Lerman

  • Analysis of social voting patterns on digg

    Kristina Lerman;Aram Galstyan

  • Stacking models for nearly optimal link prediction in complex networks.

    Amir Ghasemian;Amir Ghasemian;Amir Ghasemian;Homa Hosseinmardi;Aram Galstyan;Edoardo M. Airoldi

  • Invariant Representations without Adversarial Training

    Daniel Moyer;Shuyang Gao;Rob Brekelmans;Aram Galstyan

  • Scalable Link Prediction in Dynamic Networks via Non-Negative Matrix Factorization.

    Linhong Zhu;Greg Ver Steeg;Aram Galstyan

Frequent Co-Authors

Kristina Lerman
Kristina Lerman University of Southern California
Emilio Ferrara
Emilio Ferrara University of Southern California
Nanyun Peng
Nanyun Peng University of California, Los Angeles
Guillermo A. Cecchi
Guillermo A. Cecchi IBM (United States)
Paul R. Cohen
Paul R. Cohen University of Pittsburgh
Irina Rish
Irina Rish University of Montreal
Alexander G. Tartakovsky
Alexander G. Tartakovsky Moscow Institute of Physics and Technology
Ralph Weischedel
Ralph Weischedel University of Southern California
Paul M. Thompson
Paul M. Thompson University of Southern California
Alessandro Flammini
Alessandro Flammini Indiana University

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