World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
62
Citations
10839
World Ranking
2967
National Ranking
136

Martin Schulz 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 Martin Schulz 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: 371 publications — 84th percentile

84% of scientists in this discipline score the same or lower.

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

Martin Schulz 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 Martin Schulz 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: 62 D-Index — 80th percentile

80% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2006 - ACM Gordon Bell Prize Large-scale Electronic Structure Calculations of High-Z Metals on the BlueGene/L Platform

Overview

Martin Schulz is affiliated with the Technical University of Munich in Germany and specializes in the field of Computer Science with a focus on various subfields such as Computer Networks and Communications, Hardware and Architecture, Artificial Intelligence, Information Systems, and Signal Processing.

The research topics prominently addressed in their work include:

  • Parallel Computing and Optimization Techniques
  • Advanced Data Storage Technologies
  • Distributed and Parallel Computing Systems
  • Cloud Computing and Resource Management
  • Quantum Computing Algorithms and Architecture
  • Quantum Information and Cryptography
  • Time Series Analysis and Forecasting

Martin Schulz has contributed to numerous publications and has been active in various research venues with frequent publications in:

  • arXiv (Cornell University)
  • Academy of Management Proceedings
  • Computing in Science & Engineering
  • IEEE Transactions on Parallel and Distributed Systems
  • Parallel Computing

Some of the recent papers authored or co-authored by Martin Schulz include:

  • "Accelerating HPC With Quantum Computing: It Is a Software Challenge Too," 2022, Computing in Science & Engineering
  • "Malleability in Modern HPC Systems: Current Experiences, Challenges, and Future Opportunities," 2024, IEEE Transactions on Parallel and Distributed Systems
  • "Workflows Community Summit 2022: A Roadmap Revolution," 2023, arXiv (Cornell University)
  • "Operational Data Analytics in practice: Experiences from design to deployment in production HPC environments," 2022, Parallel Computing
  • "QMPI: A next generation MPI profiling interface for modern HPC platforms," 2020, Parallel Computing

Frequent co-authors collaborating with Martin Schulz include:

  • Martin Schreiber
  • Carsten Trinitis
  • D. M. Huber
  • Eishi Arima
  • Amir Raoofy

Martin Schulz has also contributed to book publications, notably with Springer Science+Business Media, including the title Architecture of Computing Systems published in 2022.

Among the awards received is the ACM Gordon Bell Prize awarded in 2006 for work titled "Large-scale Electronic Structure Calculations of High-Z Metals on the BlueGene/L Platform."

Best Publications

  • Adagio: making DVS practical for complex HPC applications

    Barry Rountree;David K. Lownenthal;Bronis R. de Supinski;Martin Schulz

  • Efficiently exploring architectural design spaces via predictive modeling

    Engin Ïpek;Sally A. McKee;Rich Caruana;Bronis R. de Supinski

  • Methods of inference and learning for performance modeling of parallel applications

    Benjamin C. Lee;David M. Brooks;Bronis R. de Supinski;Martin Schulz

  • Exploring Traditional and Emerging Parallel Programming Models Using a Proxy Application

    Ian Karlin;Abhinav Bhatele;Jeff Keasler;Bradford L. Chamberlain

  • Prediction models for multi-dimensional power-performance optimization on many cores

    Matthew Curtis-Maury;Ankur Shah;Filip Blagojevic;Dimitrios S. Nikolopoulos

  • A regression-based approach to scalability prediction

    Bradley J. Barnes;Barry Rountree;David K. Lowenthal;Jaxk Reeves

  • An approach to performance prediction for parallel applications

    Engin Ipek;Bronis R. de Supinski;Martin Schulz;Sally A. McKee

  • Beyond DVFS: A First Look at Performance under a Hardware-Enforced Power Bound

    Barry Rountree;Dong H. Ahn;Bronis R. de Supinski;David K. Lowenthal

  • Bounding energy consumption in large-scale MPI programs

    Barry Rountree;David K. Lowenthal;Shelby Funk;Vincent W. Freeh

  • Exploring hardware overprovisioning in power-constrained, high performance computing

    Tapasya Patki;David K. Lowenthal;Barry Rountree;Martin Schulz

  • Stack Trace Analysis for Large Scale Debugging

    D.C. Arnold;D.H. Ahn;B.R. de Supinski;G.L. Lee

  • Hybrid MPI/OpenMP power-aware computing

    Dong Li;Bronis R de Supinski;Martin Schulz;Kirk Cameron

  • XSBENCH - THE DEVELOPMENT AND VERIFICATION OF A PERFORMANCE ABSTRACTION FOR MONTE CARLO REACTOR ANALYSIS

    John R. Tramm;Andrew R. Siegel;Tanzima Islam;Martin Schulz

  • Application-level checkpointing for shared memory programs

    Greg Bronevetsky;Daniel Marques;Keshav Pingali;Peter Szwed

  • ScalaTrace: Scalable compression and replay of communication traces for high-performance computing

    Michael Noeth;Prasun Ratn;Frank Mueller;Martin Schulz

  • Analyzing and mitigating the impact of manufacturing variability in power-constrained supercomputing

    Yuichi Inadomi;Tapasya Patki;Koji Inoue;Mutsumi Aoyagi

  • Open | SpeedShop: An Open Source Infrastructure for Parallel Performance Analysis

    Martin Schulz;Jim Galarowicz;Don Maghrak;William Hachfeld

  • OMPT: An OpenMP Tools Application Programming Interface for Performance Analysis

    Alexandre E. Eichenberger;John M. Mellor-Crummey;Martin Schulz;Michael Wong

  • A Run-Time System for Power-Constrained HPC Applications

    Aniruddha Marathe;Peter E. Bailey;David K. Lowenthal;Barry Rountree

  • Practical Resource Management in Power-Constrained, High Performance Computing

    Tapasya Patki;David K. Lowenthal;Anjana Sasidharan;Matthias Maiterth

  • ScalaTrace: Scalable Compression and Replay of Communication Traces for High Performance Computing

    M Noeth;P Ratn;F Mueller;M Schulz

Frequent Co-Authors

Bronis R. de Supinski
Bronis R. de Supinski Lawrence Livermore National Laboratory
Abhinav Bhatele
Abhinav Bhatele University of Maryland, College Park
Peer-Timo Bremer
Peer-Timo Bremer Lawrence Livermore National Laboratory
Sally A. McKee
Sally A. McKee Chalmers University of Technology
Barton P. Miller
Barton P. Miller University of Wisconsin–Madison
Bernd Hamann
Bernd Hamann University of California, Davis
Frank Mueller
Frank Mueller North Carolina State University
Ganesh Gopalakrishnan
Ganesh Gopalakrishnan University of Utah
Valerio Pascucci
Valerio Pascucci University of Utah

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