World's Best Scientists 2026 revealed!
Daniel Sanchez

Daniel Sanchez

D-Index & Metrics

Computer Science

D-Index
35
Citations
5926
World Ranking
11609
National Ranking
4762

Daniel Sanchez 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 Daniel Sanchez 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: 88 publications — 5th percentile

5% of scientists in this discipline score the same or lower.

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

Daniel Sanchez 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 Daniel Sanchez 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: 35 D-Index — 20th percentile

20% of scientists in this discipline score the same or lower.

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

Overview

Daniel Sanchez is affiliated with MIT in the United States. Their research is primarily situated within the broad field of Computer Science, focusing on several subfields including Artificial Intelligence, Information Systems, Computer Networks and Communications, Computer Vision and Pattern Recognition, and Computational Theory and Mathematics.

Their main topics of work emphasize Cryptography and Data Security, Cryptography and Residue Arithmetic, Cryptographic Implementations and Security, and Chaos-based Image/Signal Encryption. Additionally, the topics cover Optimization and Search Problems, Computability, Logic, AI Algorithms, and Advanced Data Storage Technologies.

Daniel Sanchez has coauthored multiple publications with several researchers, prominently collaborating with Nikola Samardzic, Srinivas Devadas, Aleksandar Krastev, Simon Langowski, and Axel Feldmann. These partnerships reflect a consistent engagement with experts in related domains.

Their work has appeared in frequent publication venues such as IEEE Micro, Science, Proceedings of the ACM on Programming Languages, Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, and arXiv (Cornell University).

Selected recent papers include:

  • There's plenty of room at the Top: What will drive computer performance after Moore's law?, 2020, Science
  • F1: A Fast and Programmable Accelerator for Fully Homomorphic Encryption (Extended Version), 2021, arXiv (Cornell University)
  • A Tensor Compiler with Automatic Data Packing for Simple and Efficient Fully Homomorphic Encryption, 2024, Proceedings of the ACM on Programming Languages
  • Designing Hardware for Cryptography and Cryptography for Hardware, 2022, Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security
  • Leaking Secrets Through Compressed Caches, 2021, IEEE Micro

Best Publications

  • ZSim: fast and accurate microarchitectural simulation of thousand-core systems

    Daniel Sanchez;Christos Kozyrakis

  • There’s plenty of room at the Top: What will drive computer performance after Moore’s law?

    Charles E. Leiserson;Neil C. Thompson;Joel S. Emer;Joel S. Emer;Bradley C. Kuszmaul

  • Vantage: scalable and efficient fine-grain cache partitioning

    Daniel Sanchez;Christos Kozyrakis

  • The ZCache: Decoupling Ways and Associativity

    Daniel Sanchez;Christos Kozyrakis

  • F1: A Fast and Programmable Accelerator for Fully Homomorphic Encryption

    Nikola Samardzic;Axel Feldmann;Aleksandar Krastev;Srinivas Devadas

  • CraterLake: a hardware accelerator for efficient unbounded computation on encrypted data

    Unknown

  • Tailbench: a benchmark suite and evaluation methodology for latency-critical applications

    Harshad Kasture;Daniel Sanchez

  • Tarcil: reconciling scheduling speed and quality in large shared clusters

    Christina Delimitrou;Daniel Sanchez;Christos Kozyrakis

  • Ubik: efficient cache sharing with strict qos for latency-critical workloads

    Harshad Kasture;Daniel Sanchez

  • Flexible architectural support for fine-grain scheduling

    Daniel Sanchez;Richard M. Yoo;Christos Kozyrakis

  • TicToc: Time Traveling Optimistic Concurrency Control

    Xiangyao Yu;Andrew Pavlo;Daniel Sanchez;Srinivas Devadas

  • Implementing Signatures for Transactional Memory

    Daniel Sanchez;Luke Yen;Mark D. Hill;Karthikeyan Sankaralingam

  • An analysis of on-chip interconnection networks for large-scale chip multiprocessors

    Daniel Sanchez;George Michelogiannakis;Christos Kozyrakis

  • Rubik: fast analytical power management for latency-critical systems

    Harshad Kasture;Davide B. Bartolini;Nathan Beckmann;Daniel Sanchez

  • Evaluating Bufferless Flow Control for On-chip Networks

    George Michelogiannakis;Daniel Sanchez;William J. Dally;Christos Kozyrakis

  • SCD: A scalable coherence directory with flexible sharer set encoding

    Daniel Sanchez;Christos Kozyrakis

  • Exploiting locality in graph analytics through hardware-accelerated traversal scheduling

    Anurag Mukkara;Nathan Beckmann;Maleen Abeydeera;Xiaosong Ma

  • KPart: A Hybrid Cache Partitioning-Sharing Technique for Commodity Multicores

    Nosayba El-Sayed;Anurag Mukkara;Po-An Tsai;Harshad Kasture;Harshad Kasture

  • Gamma: leveraging Gustavson’s algorithm to accelerate sparse matrix multiplication

    Guowei Zhang;Nithya Attaluri;Joel S. Emer;Daniel Sanchez

  • A scalable architecture for ordered parallelism

    Mark C. Jeffrey;Suvinay Subramanian;Cong Yan;Joel Emer

  • Jigsaw: scalable software-defined caches

    Nathan Beckmann;Daniel Sanchez

  • Talus: A simple way to remove cliffs in cache performance

    Nathan Beckmann;Daniel Sanchez

Frequent Co-Authors

Christos Kozyrakis
Christos Kozyrakis Stanford University
Butler W. Lampson
Butler W. Lampson Microsoft (United States)
Chris Peikert
Chris Peikert University of Michigan–Ann Arbor
Andrew Pavlo
Andrew Pavlo Carnegie Mellon University
Michael Taylor
Michael Taylor University of Washington
William J. Dally
William J. Dally Nvidia (United Kingdom)

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