World's Best Scientists 2026 revealed!
Masoud Daneshtalab

Masoud Daneshtalab

D-Index & Metrics

Computer Science

D-Index
34
Citations
4469
World Ranking
12240
National Ranking
91

Masoud Daneshtalab 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 Masoud Daneshtalab 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: 254 publications — 64th percentile

64% of scientists in this discipline score the same or lower.

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

Masoud Daneshtalab 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 Masoud Daneshtalab 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: 34 D-Index — 16th percentile

16% of scientists in this discipline score the same or lower.

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

Overview

Masoud Daneshtalab is affiliated with Mälardalen University in Sweden. Their research primarily spans the fields of Computer Science and Engineering, with significant contributions in Artificial Intelligence, Electrical and Electronic Engineering, Computer Vision and Pattern Recognition, Hardware and Architecture, and Cardiology and Cardiovascular Medicine.

Their recent publications include:

  • A review on deep learning methods for ECG arrhythmia classification, 2020, Expert Systems with Applications X
  • Time-Sensitive Networking in automotive embedded systems: State of the art and research opportunities, 2021, Journal of Systems Architecture
  • DeepMaker: A multi-objective optimization framework for deep neural networks in embedded systems, 2020, Microprocessors and Microsystems
  • A comprehensive systematic review of integration of time sensitive networking and 5G communication, 2023, Journal of Systems Architecture
  • A Systematic Literature Review on Hardware Reliability Assessment Methods for Deep Neural Networks, 2023, ACM Computing Surveys

Masoud Daneshtalab's research topics cover a range of areas, including:

  • Advanced Neural Network Applications
  • Radiation Effects in Electronics
  • Autonomous Vehicle Technology and Safety
  • Adversarial Robustness in Machine Learning
  • Advanced Memory and Neural Computing
  • Parallel Computing and Optimization Techniques
  • ECG Monitoring and Analysis

Frequent collaborators in their research include:

  • Maksim Jenihhin
  • Jaan Raik
  • Mohammad Loni
  • Mahdi Taheri
  • Ali Zoljodi

Common venues for publishing their work are:

  • arXiv (Cornell University)
  • Journal of Systems Architecture
  • IEEE Access
  • SSRN Electronic Journal
  • Microprocessors and Microsystems

Best Publications

  • A Review on Deep Learning Methods for ECG Arrhythmia Classification

    Zahra Ebrahimi;Mohammad Loni;Masoud Daneshtalab;Arash Gharehbaghi

  • Time-Sensitive Networking in automotive embedded systems: State of the art and research opportunities

    Mohammad Ashjaei;Lucia Lo Bello;Masoud Daneshtalab;Gaetano Patti

  • Routing Algorithms in Networks-on-Chip

    Maurizio Palesi;Masoud Daneshtalab

  • EDXY - A low cost congestion-aware routing algorithm for network-on-chips

    P. Lotfi-Kamran;A. M. Rahmani;M. Daneshtalab;A. Afzali-Kusha

  • Smart hill climbing for agile dynamic mapping in many-core systems

    Mohammad Fattah;Masoud Daneshtalab;Pasi Liljeberg;Juha Plosila

  • DeepMaker: A multi-objective optimization framework for deep neural networks in embedded systems

    Mohammad Loni;Sima Sinaei;Ali Zoljodi;Masoud Daneshtalab

  • HARAQ: Congestion-Aware Learning Model for Highly Adaptive Routing Algorithm in On-Chip Networks

    Masoumeh Ebrahimi;Masoud Daneshtalab;Fahimeh Farahnakian;Juha Plosila

  • Path-Based Partitioning Methods for 3D Networks-on-Chip with Minimal Adaptive Routing

    Masoumeh Ebrahimi;Masoud Daneshtalab;Pasi Liljeberg;Juha Plosila

  • Fault-tolerant routing algorithm for 3D NoC using Hamiltonian path strategy

    Masoumeh Ebrahimi;Masoud Daneshtalab;Juha Plosila

  • CoNA: Dynamic application mapping for congestion reduction in many-core systems

    Mohamamd Fattah;Marco Ramirez;Masoud Daneshtalab;Pasi Liljeberg

  • Q-learning based congestion-aware routing algorithm for on-chip network

    Fahimeh Farahnakian;Masoumeh Ebrahimi;Masoud Daneshtalab;Pasi Liljeberg

  • CATRA- congestion aware trapezoid-based routing algorithm for on-chip networks

    Masoumeh Ebrahimi;Masoud Daneshtalab;Pasi Liljeberg;Juha Plosila

  • BARP-a dynamic routing protocol for balanced distribution of traffic in NoCs

    Pejman Lotfi-Kamran;Masoud Daneshtalab;Caro Lucas;Zainalabedin Navabi

  • DyXYZ: Fully Adaptive Routing Algorithm for 3D NoCs

    M. Ebrahimi;Xin Chang;M. Daneshtalab;J. Plosila

  • MD: Minimal path-based fault-tolerant routing in on-Chip Networks

    M. Ebrahimi;M. Daneshtalab;J. Plosila;F. Mehdipour

  • NoC Hot Spot minimization Using AntNet Dynamic Routing Algorithm

    M. Daneshtalab;A. Sobhani;A. Afzali-Kusha;O. Fatemi

  • Low-distance path-based multicast routing algorithm for network-on-chips

    Masoud Daneshtalab;Masoumeh Ebrahimi;Siamak Mohammadi;Ali Afzali-Kusha

  • Minimal-path fault-tolerant approach using connection-retaining structure in Networks-on-Chip

    Masoumeh Ebrahimi;Masoud Daneshtalab;Juha Plosila;Hannu Tenhunen

  • High Performance Fault-Tolerant Routing Algorithm for NoC-Based Many-Core Systems

    M. Ebrahimi;M. Daneshtalab;J. Plosila

  • On self-tuning networks-on-chip for dynamic network-flow dominance adaptation

    Xiaohang Wang;Mei Yang;Yingtao Jiang;Peng Liu

  • BARP-A Dynamic Routing Protocol for Balanced Distribution of Traffic in NoCs to Avoid Congestion

    P. Lotfi-Kamran;M. Daneshtalab;C. Lucas;Z. Navabi

Frequent Co-Authors

Juha Plosila
Juha Plosila University of Turku
Hannu Tenhunen
Hannu Tenhunen Royal Institute of Technology
Pasi Liljeberg
Pasi Liljeberg University of Turku
Ali Afzali-Kusha
Ali Afzali-Kusha University of Tehran
Maurizio Palesi
Maurizio Palesi University of Catania
Manoj Singh Gaur
Manoj Singh Gaur Indian Institute of Technology Jammu
Nader Bagherzadeh
Nader Bagherzadeh University of California, Irvine
Tapio Pahikkala
Tapio Pahikkala University of Turku
Sergio Saponara
Sergio Saponara University of Pisa

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