World's Best Scientists 2026 revealed!
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Electronics and Electrical Engineering
UAE
2025

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
57
Citations
12727
World Ranking
1962
National Ranking
11

Computer Science

D-Index
54
Citations
11852
World Ranking
4556
National Ranking
24

Muhammad Shafique publication distribution in Electronics and Electrical Engineering in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Electronics and Electrical Engineering in 2026. The highlighted bar marks where Muhammad Shafique sits on this spectrum.

34–53 publications: 24 scientists 54–73 publications: 52 scientists 74–93 publications: 114 scientists 94–113 publications: 203 scientists 114–133 publications: 269 scientists 134–153 publications: 355 scientists 154–173 publications: 403 scientists 174–193 publications: 445 scientists 194–213 publications: 430 scientists 214–233 publications: 431 scientists 234–253 publications: 399 scientists 254–273 publications: 366 scientists 274–293 publications: 335 scientists 294–313 publications: 300 scientists 314–333 publications: 276 scientists 334–353 publications: 250 scientists 354–373 publications: 214 scientists 374–393 publications: 187 scientists 394–413 publications: 152 scientists 414–433 publications: 169 scientists 434–453 publications: 147 scientists 454–473 publications: 111 scientists 474–493 publications: 117 scientists 494–513 publications: 103 scientists 514–533 publications: 99 scientists 534–553 publications: 92 scientists 554–573 publications: 75 scientists 574–593 publications: 58 scientists 594–613 publications: 69 scientists 614–633 publications: 50 scientists 634–653 publications: 62 scientists 654–673 publications: 54 scientists 674–693 publications: 44 scientists 694–713 publications: 37 scientists 714–733 publications: 28 scientists 734–753 publications: 26 scientists 754–773 publications: 26 scientists 774–793 publications: 19 scientists 794–813 publications: 23 scientists 814–833 publications: 20 scientists 834–853 publications: 16 scientists 854–873 publications: 20 scientists 874–893 publications: 11 scientists 894–913 publications: 11 scientists 914–933 publications: 16 scientists 934–953 publications: 13 scientists 954–973 publications: 10 scientists 974–993 publications: 11 scientists 994–1,013 publications: 9 scientists 1,014–1,033 publications: 9 scientists 1,034–1,053 publications: 10 scientists 1,054–1,064 publications: 6 scientists 1,065+ publications: 99 scientists
34 publications 1,065+

This scientist: 481 publications — 83rd percentile

83% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 1,065 publications or more.

Muhammad Shafique D-index placement in Electronics and Electrical Engineering in 2026

The chart shows the D-index (discipline H-index) distribution of Electronics and Electrical Engineering scientists ranked by Research.com in 2026. The highlighted bar marks where Muhammad Shafique sits on this spectrum.

30 D-Index: 178 scientists 31 D-Index: 257 scientists 32 D-Index: 263 scientists 33 D-Index: 262 scientists 34 D-Index: 244 scientists 35 D-Index: 236 scientists 36 D-Index: 211 scientists 37 D-Index: 220 scientists 38 D-Index: 214 scientists 39 D-Index: 214 scientists 40 D-Index: 205 scientists 41 D-Index: 187 scientists 42 D-Index: 194 scientists 43 D-Index: 201 scientists 44 D-Index: 155 scientists 45 D-Index: 189 scientists 46 D-Index: 148 scientists 47 D-Index: 160 scientists 48 D-Index: 134 scientists 49 D-Index: 130 scientists 50 D-Index: 141 scientists 51 D-Index: 156 scientists 52 D-Index: 108 scientists 53 D-Index: 130 scientists 54 D-Index: 112 scientists 55 D-Index: 97 scientists 56 D-Index: 111 scientists 57 D-Index: 102 scientists 58 D-Index: 108 scientists 59 D-Index: 120 scientists 60 D-Index: 103 scientists 61 D-Index: 93 scientists 62 D-Index: 92 scientists 63 D-Index: 74 scientists 64 D-Index: 77 scientists 65 D-Index: 73 scientists 66 D-Index: 64 scientists 67 D-Index: 69 scientists 68 D-Index: 60 scientists 69 D-Index: 39 scientists 70 D-Index: 57 scientists 71 D-Index: 59 scientists 72 D-Index: 46 scientists 73 D-Index: 49 scientists 74 D-Index: 38 scientists 75 D-Index: 35 scientists 76 D-Index: 32 scientists 77 D-Index: 35 scientists 78 D-Index: 31 scientists 79 D-Index: 22 scientists 80 D-Index: 34 scientists 81 D-Index: 31 scientists 82 D-Index: 34 scientists 83 D-Index: 23 scientists 84 D-Index: 18 scientists 85 D-Index: 30 scientists 86 D-Index: 19 scientists 87 D-Index: 19 scientists 88 D-Index: 20 scientists 89 D-Index: 8 scientists 90 D-Index: 17 scientists 91 D-Index: 7 scientists 92 D-Index: 14 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 12 scientists 97 D-Index: 10 scientists 98 D-Index: 10 scientists 99 D-Index: 12 scientists 100 D-Index: 16 scientists 101 D-Index: 5 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 8 scientists 105 D-Index: 9 scientists 106 D-Index: 13 scientists 107 D-Index: 4 scientists 108 D-Index: 5 scientists 109 D-Index: 10 scientists 110 D-Index: 8 scientists 111+ D-Index: 96 scientists
30 D-Index 111+

This scientist: 57 D-Index — 72nd percentile

72% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 111 D-Index or more.

Research.com Recognitions

  • 2025 - Research.com Electronics and Electrical Engineering in United Arab Emirates Leader Award

Overview

Muhammad Shafique is affiliated with New York University Abu Dhabi, located in the United Arab Emirates. Their research spans several core areas within computer science and engineering, emphasizing both theoretical and applied aspects of advanced computing technologies.

The primary fields of study covered by Muhammad Shafique include:

  • Computer Science
  • Engineering

Within these broader fields, their subfields of study focus on:

  • Artificial Intelligence
  • Electrical and Electronic Engineering
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Computer Networks and Communications

The main topics addressed in their work involve:

  • Advanced Memory and Neural Computing
  • Adversarial Robustness in Machine Learning
  • Ferroelectric and Negative Capacitance Devices
  • Advanced Neural Network Applications
  • Quantum Computing Algorithms and Architecture
  • Anomaly Detection Techniques and Applications
  • Physical Unclonable Functions (PUFs) and Hardware Security

They have published extensively in various venues. Among the most frequent publication venues are:

  • arXiv (Cornell University)
  • IEEE Access
  • IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
  • ACM Transactions on Embedded Computing Systems
  • Microprocessors and Microsystems

Frequent coauthors in Muhammad Shafique's collaborations include:

  • Alberto Marchisio
  • Muhammad Abdullah Hanif
  • Rachmad Vidya Wicaksana Putra
  • Maurizio Martina
  • Nouhaila Innan

Their recent papers highlight a focus on both hardware and software aspects of deep learning acceleration, as well as energy management in smart grids. Representative publications include:

  • "Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead" (2020), IEEE Access
  • "An Updated Survey of Efficient Hardware Architectures for Accelerating Deep Convolutional Neural Networks" (2020), Future Internet
  • "A Systematic Review Towards Integrative Energy Management of Smart Grids and Urban Energy Systems" (2023), Renewable and Sustainable Energy Reviews
  • "High-Performance Accurate and Approximate Multipliers for FPGA-Based Hardware Accelerators" (2021), IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
  • "A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends" (2024), IEEE Access

Best Publications

  • Mapping on multi/many-core systems: survey of current and emerging trends

    Amit Kumar Singh;Muhammad Shafique;Akash Kumar;Jorg Henkel

  • A low latency generic accuracy configurable adder

    Muhammad Shafique;Waqas Ahmad;Rehan Hafiz;Jorg Henkel

  • Reliable on-chip systems in the nano-era: lessons learnt and future trends

    Jorg Henkel;Lars Bauer;Nikil Dutt;Puneet Gupta

  • The EDA Challenges in the Dark Silicon Era: Temperature, Reliability, and Variability Perspectives

    Muhammad Shafique;Siddharth Garg;Jörg Henkel;Diana Marculescu

  • Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead

    Maurizio Capra;Beatrice Bussolino;Alberto Marchisio;Guido Masera

  • An Updated Survey of Efficient Hardware Architectures for Accelerating Deep Convolutional Neural Networks

    Maurizio Capra;Beatrice Bussolino;Alberto Marchisio;Muhammad Shafique

  • Architectural-space exploration of approximate multipliers

    Semeen Rehman;Walaa El-Harouni;Muhammad Shafique;Akash Kumar

  • A Miniaturized Flexible Frequency Selective Surface for X-Band Applications

    Mudassar Nauman;Rashid Saleem;Amir Khurrum Rashid;Muhammad Farhan Shafique

  • Reliable software for unreliable hardware: embedded code generation aiming at reliability

    Semeen Rehman;Muhammad Shafique;Florian Kriebel;Jorg Henkel

  • A Roadmap Toward the Resilient Internet of Things for Cyber-Physical Systems

    Denise Ratasich;Faiq Khalid;Florian Geissler;Radu Grosu

  • Invited - Cross-layer approximate computing: from logic to architectures

    Muhammad Shafique;Rehan Hafiz;Semeen Rehman;Walaa El-Harouni

  • Robust Machine Learning Systems: Challenges,Current Trends, Perspectives, and the Road Ahead

    Muhammad Shafique;Mahum Naseer;Theocharis Theocharides;Christos Kyrkou

  • TSP: thermal safe power: efficient power budgeting for many-core systems in dark silicon

    Santiago Pagani;Heba Khdr;Waqaas Munawar;Jian-Jia Chen

  • New trends in dark silicon

    Jorg Henkel;Heba Khdr;Santiago Pagani;Muhammad Shafique

  • Performance evaluation of convolutional neural network for hand gesture recognition using EMG

    Ali Raza Asif;Asim Waris;Syed Omer Gilani;Mohsin Jamil;Mohsin Jamil

  • ALWANN: Automatic Layer-Wise Approximation of Deep Neural Network Accelerators without Retraining

    Vojtech Mrazek;Zdenek Vasicek;Lukas Sekanina;Muhammad Abdullah Hanif

  • High-Performance Accurate and Approximate Multipliers for FPGA-based Hardware Accelerators

    Salim Ullah;Semeen Rehman;Muhammad Shafique;Akash Kumar

  • Compact ultra-wideband diversity antenna with a floating parasitic digitated decoupling structure

    Muhammad Saeed Khan;Antonio-Daniele Capobianco;Ali Imran Najam;Imran Shoaib

  • Probabilistic Error Modeling for Approximate Adders

    Sana Mazahir;Osman Hasan;Rehan Hafiz;Muhammad Shafique

  • Polarization Insensitive Dual Band Frequency Selective Surface for RF Shielding Through Glass Windows

    Umer Farooq;Muhammad Farhan Shafique;Muhammad Junaid Mughal

  • An adaptive complexity reduction scheme with fast prediction unit decision for HEVC intra encoding

    Muhammad Usman Karim Khan;Muhammad Shafique;Jorg Henkel

  • An overview of next-generation architectures for machine learning: Roadmap, opportunities and challenges in the IoT era

    Muhammad Shafique;Theocharis Theocharides;Christos-Savvas Bouganis;Muhammad Abdullah Hanif

  • FT-ClipAct: resilience analysis of deep neural networks and improving their fault tolerance using clipped activation

    Le-Ha Hoang;Muhammad Abdullah Hanif;Muhammad Shafique

Frequent Co-Authors

Jorg Henkel
Jorg Henkel Karlsruhe Institute of Technology
Osman Hasan
Osman Hasan National University of Sciences and Technology
Jian-Jia Chen
Jian-Jia Chen TU Dortmund University
Sri Parameswaran
Sri Parameswaran University of Sydney
Tulika Mitra
Tulika Mitra National University of Singapore
Akash Kumar
Akash Kumar TU Dresden
Lukas Sekanina
Lukas Sekanina Brno University of Technology
Norbert Wehn
Norbert Wehn Technical University of Kaiserslautern
Jürgen Teich
Jürgen Teich University of Erlangen-Nuremberg

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Exploring these related degrees and career pathways can complement an education in Electronics and Electrical Engineering, aligning skills with industry demands and personal work preferences.

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