World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
76
Citations
70814
World Ranking
1307
National Ranking
692

Matei Zaharia 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 Matei Zaharia 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: 208 publications — 49th percentile

49% of scientists in this discipline score the same or lower.

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

Matei Zaharia 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 Matei Zaharia 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: 76 D-Index — 91st percentile

91% of scientists in this discipline score the same or lower.

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

Overview

Matei Zaharia is a researcher affiliated with the University of California, Berkeley in the United States. Their academic work primarily focuses on the field of Computer Science, with significant contributions in several subfields including Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition, Information Systems, and Cardiology and Cardiovascular Medicine.

Their research covers a variety of topics, notably:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Cloud Computing and Resource Management
  • Multimodal Machine Learning Applications
  • Advanced Data Storage Technologies
  • Distributed systems and fault tolerance
  • Advanced Image and Video Retrieval Techniques

Among their recent publications are:

  • "Advances, challenges and opportunities in creating data for trustworthy AI," 2022, published in Nature Machine Intelligence
  • "How Is ChatGPT's Behavior Changing Over Time?," 2024, published in Harvard Data Science Review
  • "ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction," 2022, published in Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  • "ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT," 2020, published on arXiv (Cornell University)
  • "Delta lake," 2020, published in Proceedings of the VLDB Endowment

Zaharia's frequent co-authors include:

  • Peter Bailis
  • Omar Khattab
  • Albert J. Rogers
  • Sanjiv M. Narayan
  • James Zou

They have published extensively in venues such as:

  • arXiv (Cornell University)
  • Proceedings of the VLDB Endowment
  • Circulation
  • Heart Rhythm
  • Proceedings of the 2022 International Conference on Management of Data

Best Publications

  • A view of cloud computing

    Michael Armbrust;Armando Fox;Rean Griffith;Anthony D. Joseph

  • Above the Clouds: A Berkeley View of Cloud Computing

    Michael Armbrust;Armando Fox;Rean Griffith;Anthony D. Joseph

  • Spark: cluster computing with working sets

    Matei Zaharia;Mosharaf Chowdhury;Michael J. Franklin;Scott Shenker

  • Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing

    Matei Zaharia;Mosharaf Chowdhury;Tathagata Das;Ankur Dave

  • Apache Spark: a unified engine for big data processing

    Matei Zaharia;Reynold S. Xin;Patrick Wendell;Tathagata Das

  • Improving MapReduce performance in heterogeneous environments

    Matei Zaharia;Andy Konwinski;Anthony D. Joseph;Randy Katz

  • Mesos: a platform for fine-grained resource sharing in the data center

    Benjamin Hindman;Andy Konwinski;Matei Zaharia;Ali Ghodsi

  • On the Opportunities and Risks of Foundation Models.

    Rishi Bommasani;Drew A. Hudson;Ehsan Adeli;Russ Altman

  • MLlib: machine learning in apache spark

    Xiangrui Meng;Joseph Bradley;Burak Yavuz;Evan Sparks

  • Delay scheduling: a simple technique for achieving locality and fairness in cluster scheduling

    Matei Zaharia;Dhruba Borthakur;Joydeep Sen Sarma;Khaled Elmeleegy

  • Spark SQL: Relational Data Processing in Spark

    Michael Armbrust;Reynold S. Xin;Cheng Lian;Yin Huai

  • Dominant resource fairness: fair allocation of multiple resource types

    Ali Ghodsi;Matei Zaharia;Benjamin Hindman;Andy Konwinski

  • Discretized streams: fault-tolerant streaming computation at scale

    Matei Zaharia;Tathagata Das;Haoyuan Li;Timothy Hunter

  • ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT

    Omar Khattab;Matei Zaharia

  • Sparrow: distributed, low latency scheduling

    Kay Ousterhout;Patrick Wendell;Matei Zaharia;Ion Stoica

  • Managing data transfers in computer clusters with orchestra

    Mosharaf Chowdhury;Matei Zaharia;Justin Ma;Michael I. Jordan

  • Discretized streams: an efficient and fault-tolerant model for stream processing on large clusters

    Matei Zaharia;Tathagata Das;Haoyuan Li;Scott Shenker

  • PipeDream: generalized pipeline parallelism for DNN training

    Deepak Narayanan;Aaron Harlap;Amar Phanishayee;Vivek Seshadri

  • Shark: SQL and rich analytics at scale

    Reynold S. Xin;Josh Rosen;Matei Zaharia;Michael J. Franklin

  • Advances, challenges and opportunities in creating data for trustworthy AI

    Unknown

  • Efficient large-scale language model training on GPU clusters using megatron-LM

    Deepak Narayanan;Mohammad Shoeybi;Jared Casper;Patrick LeGresley

Frequent Co-Authors

Peter Bailis
Peter Bailis Stanford University
Ion Stoica
Ion Stoica University of California, Berkeley
Scott Shenker
Scott Shenker University of California, Berkeley
Ali Ghodsi
Ali Ghodsi University of Waterloo
Michael J. Franklin
Michael J. Franklin University of Chicago
Srinivasan Keshav
Srinivasan Keshav University of Cambridge
Shivaram Venkataraman
Shivaram Venkataraman University of Wisconsin–Madison
Anthony D. Joseph
Anthony D. Joseph University of California, Berkeley
David A. Patterson
David A. Patterson University of California, Berkeley
Randy H. Katz
Randy H. Katz University of California, Berkeley

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