World's Best Scientists 2026 revealed!
Vijay Janapa Reddi

Vijay Janapa Reddi

D-Index & Metrics

Computer Science

D-Index
45
Citations
13900
World Ranking
7030
National Ranking
3083

Vijay Janapa Reddi 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 Vijay Janapa Reddi 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: 202 publications — 47th percentile

47% of scientists in this discipline score the same or lower.

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

Vijay Janapa Reddi 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 Vijay Janapa Reddi 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: 45 D-Index — 51st percentile

51% of scientists in this discipline score the same or lower.

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

Overview

Vijay Janapa Reddi is affiliated with Harvard University in the United States and has made contributions primarily in the fields of Computer Science and Engineering. Their work is distributed across multiple subfields including Artificial Intelligence, Electrical and Electronic Engineering, Computer Vision and Pattern Recognition, Computer Networks and Communications, and Hardware and Architecture.

Their research addresses a range of main topics such as Advanced Memory and Neural Computing, Advanced Neural Network Applications, Robotics and Sensor-Based Localization, Parallel Computing and Optimization Techniques, Robotic Path Planning Algorithms, Adversarial Robustness in Machine Learning, and IoT and Edge/Fog Computing.

Vijay Janapa Reddi has collaborated frequently with a group of coauthors, including Srivatsan Krishnan, Brian Plancher, Zishen Wan, Colby Banbury, and Aleksandra Faust.

The scientist's publication record features significant contributions to various venues. The most frequent publication outlets include:

  • arXiv (Cornell University)
  • IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
  • Zenodo (CERN European Organization for Nuclear Research)
  • IEEE Micro
  • Communications of the ACM

Among the recent papers attributed to Vijay Janapa Reddi are:

  • Deep Reinforcement Learning for Cyber Security, 2021, IEEE Transactions on Neural Networks and Learning Systems
  • Benchmarking TinyML Systems: Challenges and Direction, 2020, arXiv (Cornell University)
  • TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems, 2020, arXiv (Cornell University)
  • MLPerf: An Industry Standard Benchmark Suite for Machine Learning Performance, 2020, IEEE Micro
  • MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers, 2020, arXiv (Cornell University)

Best Publications

  • Pin: building customized program analysis tools with dynamic instrumentation

    Chi-Keung Luk;Robert Cohn;Robert Muth;Harish Patil

  • GPUWattch: enabling energy optimizations in GPGPUs

    Jingwen Leng;Tayler Hetherington;Ahmed ElTantawy;Syed Gilani

  • Deep Reinforcement Learning for Cyber Security.

    Thanh Thi Nguyen;Vijay Janapa Reddi

  • MLPerf inference benchmark

    Vijay Janapa Reddi;Christine Cheng;David Kanter;Peter Mattson

  • A Dynamic Compilation Framework for Controlling Microprocessor Energy and Performance

    Qiang Wu;Margaret Martonosi;Douglas W. Clark;V. J. Reddi

  • Web search using mobile cores: quantifying and mitigating the price of efficiency

    Vijay Janapa Reddi;Benjamin C. Lee;Trishul Chilimbi;Kushagra Vaid

  • PIN: a binary instrumentation tool for computer architecture research and education

    Vijay Janapa Reddi;Alex Settle;Daniel A. Connors;Robert S. Cohn

  • Benchmarking TinyML Systems: Challenges and Direction

    Colby R. Banbury;Vijay Janapa Reddi;Max Lam;William Fu

  • PLR: A Software Approach to Transient Fault Tolerance for Multicore Architectures

    A. Shye;J. Blomstedt;T. Moseley;V.J. Reddi

  • High-performance and energy-efficient mobile web browsing on big/little systems

    Yuhao Zhu;V. J. Reddi

  • MLPerf Training Benchmark.

    Peter Mattson;Christine Cheng;Cody Coleman;Greg Diamos

  • Mobile CPU's rise to power: Quantifying the impact of generational mobile CPU design trends on performance, energy, and user satisfaction

    Matthew Halpern;Yuhao Zhu;Vijay Janapa Reddi

  • TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems

    Robert David;Jared Duke;Advait Jain;Vijay Janapa Reddi

  • Using Process-Level Redundancy to Exploit Multiple Cores for Transient Fault Tolerance

    A. Shye;T. Moseley;V.J. Reddi;J. Blomstedt

  • MLPerf: An Industry Standard Benchmark Suite for Machine Learning Performance

    Peter Mattson;Hanlin Tang;Gu-Yeon Wei;Carole-Jean Wu

  • Voltage emergency prediction: Using signatures to reduce operating margins

    Vijay Janapa Reddi;Meeta S. Gupta;Glenn Holloway;Gu-Yeon Wei

  • MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers

    Colby R. Banbury;Chuteng Zhou;Igor Fedorov;Ramon Matas Navarro

  • Event-based scheduling for energy-efficient QoS (eQoS) in mobile Web applications

    Yuhao Zhu;Matthew Halpern;Vijay Janapa Reddi

  • Shadow Profiling: Hiding Instrumentation Costs with Parallelism

    Tipp Moseley;Alex Shye;Vijay Janapa Reddi;Dirk Grunwald

  • Voltage Smoothing: Characterizing and Mitigating Voltage Noise in Production Processors via Software-Guided Thread Scheduling

    Vijay Janapa Reddi;Svilen Kanev;Wonyoung Kim;Simone Campanoni

  • MLPerf Training Benchmark

    Peter Mattson;Christine Cheng;Gregory F. Diamos;Cody Coleman

  • HELIX: automatic parallelization of irregular programs for chip multiprocessing

    Simone Campanoni;Timothy Jones;Glenn Holloway;Vijay Janapa Reddi

  • Proceedings of the 2017 International Symposium on Code Generation and Optimization

    Vijay Janapa Reddi;Aaron Smith;Lingjia Tang

Frequent Co-Authors

David Brooks
David Brooks Harvard University
Gu-Yeon Wei
Gu-Yeon Wei Harvard University
Gennady Pekhimenko
Gennady Pekhimenko University of Toronto
Carole-Jean Wu
Carole-Jean Wu Meta Platforms, Inc.
Alper Buyuktosunoglu
Alper Buyuktosunoglu IBM (United States)
Pradip Bose
Pradip Bose IBM (United States)
Peter Bailis
Peter Bailis Stanford University
David A. Patterson
David A. Patterson University of California, Berkeley
Kim Hazelwood
Kim Hazelwood Facebook (United States)
Arijit Raychowdhury
Arijit Raychowdhury Georgia Institute of Technology

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