World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
30
Citations
4612
World Ranking
13990
National Ranking
5558

Louis-Noël Pouchet 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 Louis-Noël Pouchet 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: 111 publications — 12th percentile

12% of scientists in this discipline score the same or lower.

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

Louis-Noël Pouchet 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 Louis-Noël Pouchet 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: 30 D-Index — 3rd percentile

3% of scientists in this discipline score the same or lower.

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

Overview

Louis-Noël Pouchet is affiliated with Colorado State University in the United States and specializes in computer science with a focus on hardware and architecture, information systems, and computer networks and communications. Their research also spans areas such as software, electrical and electronic engineering, parallel computing, embedded systems design, software engineering, software testing, formal methods in verification, interconnection networks, and logic programming.

The scientist's recent publications reflect a focus on software engineering and hardware design, emphasizing optimization and formal verification techniques. Notable papers include:

  • "Self-Supervised Learning to Prove Equivalence Between Straight-Line Programs via Rewrite Rules" (2023), published in IEEE Transactions on Software Engineering
  • "Accelerator design with decoupled hardware customizations" (2022), Proceedings of the 59th ACM/IEEE Design Automation Conference
  • "Automatic Hardware Pragma Insertion in High-Level Synthesis: A Non-Linear Programming Approach" (2025), ACM Transactions on Design Automation of Electronic Systems
  • "Equivalence of Dataflow Graphs via Rewrite Rules Using a Graph-to-Sequence Neural Model" (2020), arXiv (Cornell University)
  • "Optimizing Coherence Traffic in Manycore Processors Using Closed-Form Caching/Home Agent Mappings" (2021), IEEE Access

Louis-Noël Pouchet has frequently published in venues such as arXiv (Cornell University), ACM Transactions on Design Automation of Electronic Systems, IEEE Transactions on Software Engineering, the ACM/IEEE Design Automation Conference, and IEEE Access.

Frequent collaborators include Steve Kommrusch, Gabriel Rodríguez, Juan Touriño, Stéphane Pouget, and Théo Barollet, indicating active partnerships primarily in the fields of software engineering and hardware design.

Main topics of their work encompass:

  • Parallel Computing and Optimization Techniques
  • Embedded Systems Design Techniques
  • Software Engineering Research
  • Software Testing and Debugging Techniques
  • Formal Methods in Verification
  • Interconnection Networks and Systems
  • Logic, programming, and type systems

Louis-Noël Pouchet's research contributions bridge both theoretical and applied aspects of computer science, with particular emphasis on improving hardware synthesis processes and software equivalence verification through formal and machine learning methods.

Best Publications

  • SequenceR : Sequence-to-Sequence Learning for End-to-End Program Repair

    Zimin Chen;Steve Kommrusch;Michele Tufano;Louis-Noel Pouchet

  • High-performance code generation for stencil computations on GPU architectures

    Justin Holewinski;Louis-Noël Pouchet;P. Sadayappan

  • The polyhedral model is more widely applicable than you think

    Mohamed-Walid Benabderrahmane;Louis-Noël Pouchet;Albert Cohen;Cédric Bastoul

  • Polly – Polyhedral optimization in LLVM

    Tobias Grosser;Hongbin Zheng;Raghesh Aloor;Andreas Simburger

  • Iterative optimization in the polyhedral model: part ii, multidimensional time

    Louis-Noël Pouchet;Cédric Bastoul;Albert Cohen;John Cavazos

  • Polyhedral-based data reuse optimization for configurable computing

    Louis-Noel Pouchet;Peng Zhang;P. Sadayappan;Jason Cong

  • Iterative Optimization in the Polyhedral Model: Part I, One-Dimensional Time

    Louis-Noel Pouchet;Cedric Bastoul;Albert Cohen;Nicolas Vasilache

  • A stencil compiler for short-vector SIMD architectures

    Tom Henretty;Richard Veras;Franz Franchetti;Louis-Noël Pouchet

  • When polyhedral transformations meet SIMD code generation

    Martin Kong;Richard Veras;Kevin Stock;Franz Franchetti

  • Data layout transformation for stencil computations on short-vector SIMD architectures

    Tom Henretty;Kevin Stock;Louis-Noël Pouchet;Franz Franchetti

  • Loop transformations: convexity, pruning and optimization

    Louis-Noël Pouchet;Uday Bondhugula;Cédric Bastoul;Albert Cohen

  • Automatic Selection of Sparse Matrix Representation on GPUs

    Naser Sedaghati;Te Mu;Louis-Noel Pouchet;Srinivasan Parthasarathy

  • Predictive Modeling in a Polyhedral Optimization Space

    Eunjung Park;John Cavazos;Louis-Noël Pouchet;Louis-Noël Pouchet;Cédric Bastoul

  • Combined Iterative and Model-driven Optimization in an Automatic Parallelization Framework

    Louis-Noël Pouchet;Uday Bondhugula;Cédric Bastoul;Albert Cohen

  • Using machine learning to improve automatic vectorization

    Kevin Stock;Louis-Noël Pouchet;P. Sadayappan

  • A framework for enhancing data reuse via associative reordering

    Kevin Stock;Martin Kong;Tobias Grosser;Louis-Noël Pouchet

  • Dynamic trace-based analysis of vectorization potential of applications

    Justin Holewinski;Ragavendar Ramamurthi;Mahesh Ravishankar;Naznin Fauzia

  • Analytical bounds for optimal tile size selection

    Jun Shirako;Kamal Sharma;Naznin Fauzia;Louis-Noël Pouchet

  • Code generation for parallel execution of a class of irregular loops on distributed memory systems

    Mahesh Ravishankar;John Eisenlohr;Louis-Noël Pouchet;J. Ramanujam

  • Improving polyhedral code generation for high-level synthesis

    Wei Zuo;Peng Li;Deming Chen;Louis-Noel Pouchet

Frequent Co-Authors

P. Sadayappan
P. Sadayappan University of Utah
J. Ramanujam
J. Ramanujam Louisiana State University
Atanas Rountev
Atanas Rountev The Ohio State University
Albert Cohen
Albert Cohen Google (United States)
Sriram Krishnamoorthy
Sriram Krishnamoorthy University of California, Santa Barbara
Robert J. Harrison
Robert J. Harrison Murdoch University
Vivek Sarkar
Vivek Sarkar Georgia Institute of Technology
Jason Cong
Jason Cong University of California, Los Angeles
Franz Franchetti
Franz Franchetti Carnegie Mellon University
Deming Chen
Deming Chen University of Illinois at Urbana-Champaign

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