World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
30
Citations
3822
World Ranking
14097
National Ranking
513

Gianluca Palermo 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 Gianluca Palermo 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: 191 publications — 43rd percentile

43% of scientists in this discipline score the same or lower.

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

Gianluca Palermo 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 Gianluca Palermo 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

Gianluca Palermo is affiliated with the Polytechnic University of Milan in Italy and works primarily in the field of Computer Science. Their research focuses across several subfields, including Computational Theory and Mathematics, Molecular Biology, Computer Networks and Communications, Hardware and Architecture, and Artificial Intelligence.

Palermo's research contributions span a range of topics, notably:

  • Computational Drug Discovery Methods
  • Parallel Computing and Optimization Techniques
  • Protein Structure and Dynamics
  • Cloud Computing and Resource Management
  • Machine Learning in Materials Science
  • Innovative Microfluidic and Catalytic Techniques Innovation
  • Distributed and Parallel Computing Systems

The scientist has published extensively, including papers such as:

  • "A Review on Parallel Virtual Screening Softwares for High-Performance Computers" (2022) in Pharmaceuticals
  • "Addressing docking pose selection with structure-based deep learning: Recent advances, challenges and opportunities" (2024) in Computational and Structural Biotechnology Journal
  • "EXSCALATE: An Extreme-Scale Virtual Screening Platform for Drug Discovery Targeting Polypharmacology to Fight SARS-CoV-2" (2022) in IEEE Transactions on Emerging Topics in Computing
  • "EVEREST: A design environment for extreme-scale big data analytics on heterogeneous platforms" (2021) in arXiv (Cornell University)
  • "GPU-optimized approaches to molecular docking-based virtual screening in drug discovery: A comparative analysis" (2023) in Journal of Parallel and Distributed Computing

Publication venues where Palermo has frequently contributed include:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • Journal of Parallel and Distributed Computing
  • Computational and Structural Biotechnology Journal
  • Quantum Science and Technology

Their collaborations are marked by frequent co-authorship with several researchers, notably:

  • Davide Gadioli
  • Andrea R. Beccari
  • Emanuele Vitali
  • Roberto Rocco
  • Federico Ficarelli

Best Publications

  • A Survey on Compiler Autotuning using Machine Learning

    Amir H. Ashouri;William Killian;John Cavazos;Gianluca Palermo

  • ReSPIR: A Response Surface-Based Pareto Iterative Refinement for Application-Specific Design Space Exploration

    G. Palermo;C. Silvano;V. Zaccaria

  • AES power attack based on induced cache miss and countermeasure

    G. Bertoni;V. Zaccaria;L. Breveglieri;M. Monchiero

  • Secure Memory Accesses on Networks-on-Chip

    L. Fiorin;G. Palermo;S. Lukovic;V. Catalano

  • Exploration of distributed shared memory architectures for NoC-based multiprocessors

    Matteo Monchiero;Gianluca Palermo;Cristina Silvano;Oreste Villa

  • Multi-objective design space exploration of embedded systems

    Gianluca Palermo;Cristina Silvano;Vittorio Zaccaria

  • MiCOMP: Mitigating the Compiler Phase-Ordering Problem Using Optimization Sub-Sequences and Machine Learning

    Amir H. Ashouri;Andrea Bignoli;Gianluca Palermo;Cristina Silvano

  • A security monitoring service for NoCs

    Leandro Fiorin;Gianluca Palermo;Cristina Silvano

  • MULTICUBE: Multi-objective Design Space Exploration of Multi-core Architectures

    C. Silvano;W. Fornaciari;G. Palermo;V. Zaccaria

  • COBAYN: Compiler Autotuning Framework Using Bayesian Networks

    Amir Hossein Ashouri;Giovanni Mariani;Gianluca Palermo;Eunjung Park

  • PIRATE: A Framework for Power/Performance Exploration of Network-on-Chip Architectures

    Gianluca Palermo;Cristina Silvano

  • An industrial design space exploration framework for supporting run-time resource management on multi-core systems

    G. Mariani;P. Avasare;G. Vanmeerbeeck;C. Ykman-Couvreur

  • The COMPLEX methodology for UML/MARTE Modeling and design space exploration of embedded systems

    Fernando Herrera;Héctor Posadas;Pablo Peñil;Eugenio Villar

  • Efficient Synchronization for Embedded On-Chip Multiprocessors

    M. Monchiero;G. Palermo;C. Silvano;O. Villa

  • A correlation-based design space exploration methodology for multi-processor systems-on-chip

    Giovanni Mariani;Aleksandar Brankovic;Gianluca Palermo;Jovana Jovic

  • Linking run-time resource management of embedded multi-core platforms with automated design-time exploration

    Chantal Ykman-Couvreur;Prabhat Avasare;Giovanni Mariani;Gianluca Palermo

  • A Review on Parallel Virtual Screening Softwares for High Performance Computers.

    Natarajan Arul Murugan;Artur Podobas;Davide Gadioli;Emanuele Vitali

  • A Pipelined Fast 2D-DCT Accelerator for FPGA-based SoCs

    A. Tumeo;M. Monchiero;G. Palermo;F. Ferrandi

  • The COMPLEX reference framework for HW/SW co-design and power management supporting platform-based design-space exploration

    Kim Grüttner;Philipp A. Hartmann;Kai Hylla;Sven Rosinger

  • Discrete Particle Swarm Optimization for Multi-objective Design Space Exploration

    G. Palermo;C. Silvano;V. Zaccaria

  • Efficiency and scalability of barrier synchronization on NoC based many-core architectures

    Oreste Villa;Gianluca Palermo;Cristina Silvano

Frequent Co-Authors

Cristina Silvano
Cristina Silvano Polytechnic University of Milan
Donatella Sciuto
Donatella Sciuto Polytechnic University of Milan
Luca Benini
Luca Benini ETH Zurich
Luigi Carro
Luigi Carro Federal University of Rio Grande do Sul
Petri Mahonen
Petri Mahonen Aalto University
Dimitrios Soudris
Dimitrios Soudris National Technical University of Athens
Gerd Ascheid
Gerd Ascheid RWTH Aachen University
Heinrich Meyr
Heinrich Meyr RWTH Aachen University
Pier Luca Lanzi
Pier Luca Lanzi Polytechnic University of Milan

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