World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
6724
World Ranking
10176
National Ranking
4284

Jason Mars 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 Jason Mars 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 100 publications — 8th percentile

8% of scientists in this discipline score the same or lower.

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

Jason Mars 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 Jason Mars sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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: 38 D-Index — 30th percentile

30% of scientists in this discipline score the same or lower.

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

Overview

Jason Mars is affiliated with the University of Michigan-Ann Arbor in the United States. Their research is primarily situated within the field of Computer Science, with a strong focus on Artificial Intelligence. Other subfields include Information Systems, Computer Networks and Communications, Information Systems and Management, and Computer Vision and Pattern Recognition.

The scientist's work encompasses several major topics, notably:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Software System Performance and Reliability
  • Cloud Computing and Resource Management
  • Scientific Computing and Data Management
  • Hate Speech and Cyberbullying Detection
  • Software Engineering Research

Jason Mars has published extensively, with frequent contributions to the following venues:

  • arXiv (Cornell University)
  • International Journal of Artificial Intelligence and Robotics Research
  • IEEE Computer Architecture Letters
  • Findings of the Association for Computational Linguistics: ACL 2022
  • Proceedings of the ACM on Programming Languages

Some of their recent papers include:

  • "Rule By Example: Harnessing Logical Rules for Explainable Hate Speech Detection," 2024, International Journal of Artificial Intelligence and Robotics Research
  • "Towards Personalized Intelligence at Scale," 2022, arXiv (Cornell University)
  • "The Jaseci Programming Paradigm and Runtime Stack: Building Scale-Out Production Applications Easy and Fast," 2023, IEEE Computer Architecture Letters
  • "Scaling Down to Scale Up: A Cost-Benefit Analysis of Replacing OpenAI's LLM with Open Source SLMs in Production," 2023, arXiv (Cornell University)
  • "One Agent Too Many: User Perspectives on Approaches to Multi-agent Conversational AI," 2024, arXiv (Cornell University)

Jason Mars frequently collaborates with several researchers, including:

  • Lingjia Tang
  • Yiping Kang
  • Krisztián Flautner
  • Christopher Clarke
  • Ashish Mahendra

Best Publications

  • Bubble-Up: increasing utilization in modern warehouse scale computers via sensible co-locations

    Jason Mars;Lingjia Tang;Robert Hundt;Kevin Skadron

  • Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge

    Yiping Kang;Johann Hauswald;Cao Gao;Austin Rovinski

  • Bubble-flux: precise online QoS management for increased utilization in warehouse scale computers

    Hailong Yang;Alex Breslow;Jason Mars;Lingjia Tang

  • The Architectural Implications of Autonomous Driving: Constraints and Acceleration

    Shih-Chieh Lin;Yunqi Zhang;Chang-Hong Hsu;Matt Skach

  • An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction

    Stefan Larson;Anish Mahendran;Joseph J. Peper;Christopher Clarke

  • Sirius: An Open End-to-End Voice and Vision Personal Assistant and Its Implications for Future Warehouse Scale Computers

    Johann Hauswald;Michael A. Laurenzano;Yunqi Zhang;Cheng Li

  • The impact of memory subsystem resource sharing on datacenter applications

    Lingjia Tang;Jason Mars;Neil Vachharajani;Robert Hundt

  • DjiNN and Tonic: DNN as a service and its implications for future warehouse scale computers

    Johann Hauswald;Yiping Kang;Michael A. Laurenzano;Quan Chen

  • Whare-map: heterogeneity in "homogeneous" warehouse-scale computers

    Jason Mars;Lingjia Tang

  • SMiTe: Precise QoS Prediction on Real-System SMT Processors to Improve Utilization in Warehouse Scale Computers

    Yunqi Zhang;Michael A. Laurenzano;Jason Mars;Lingjia Tang

  • Gist: efficient data encoding for deep neural network training

    Animesh Jain;Amar Phanishayee;Jason Mars;Lingjia Tang

  • GrandSLAm: Guaranteeing SLAs for Jobs in Microservices Execution Frameworks

    Ram Srivatsa Kannan;Lavanya Subramanian;Ashwin Raju;Jeongseob Ahn

  • Prophet: Precise QoS Prediction on Non-Preemptive Accelerators to Improve Utilization in Warehouse-Scale Computers

    Quan Chen;Hailong Yang;Minyi Guo;Ram Srivatsa Kannan

  • Contention aware execution: online contention detection and response

    Jason Mars;Neil Vachharajani;Robert Hundt;Mary Lou Soffa

  • Adrenaline: Pinpointing and reining in tail queries with quick voltage boosting

    Chang-Hong Hsu;Yunqi Zhang;Michael A. Laurenzano;David Meisner

  • Baymax: QoS Awareness and Increased Utilization for Non-Preemptive Accelerators in Warehouse Scale Computers

    Quan Chen;Hailong Yang;Jason Mars;Lingjia Tang

  • Heterogeneity in “Homogeneous” Warehouse-Scale Computers: A Performance Opportunity

    J. Mars;Lingjia Tang;R. Hundt

  • Octopus-Man: QoS-driven task management for heterogeneous multicores in warehouse-scale computers

    Vinicius Petrucci;Michael A. Laurenzano;John Doherty;Yunqi Zhang

  • Compiling for niceness: mitigating contention for QoS in warehouse scale computers

    Lingjia Tang;Jason Mars;Mary Lou Soffa

  • Evaluating Indirect Branch Handling Mechanisms in Software Dynamic Translation Systems

    Jason D. Hiser;Daniel Williams;Wei Hu;Jack W. Davidson

  • Directly characterizing cross core interference through contention synthesis

    Jason Mars;Lingjia Tang;Mary Lou Soffa

Frequent Co-Authors

Lingjia Tang
Lingjia Tang University of Michigan–Ann Arbor
Mary Lou Soffa
Mary Lou Soffa University of Virginia
Ronald G. Dreslinski
Ronald G. Dreslinski University of Michigan–Ann Arbor
Trevor Mudge
Trevor Mudge University of Michigan–Ann Arbor
Scott Mahlke
Scott Mahlke University of Michigan–Ann Arbor
Dean M. Tullsen
Dean M. Tullsen University of California, San Diego
Jack W. Davidson
Jack W. Davidson University of Virginia
Thomas F. Wenisch
Thomas F. Wenisch University of Michigan–Ann Arbor
Kevin Skadron
Kevin Skadron University of Virginia
Danny H. K. Tsang
Danny H. K. Tsang Hong Kong University of Science and Technology

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