World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
58
Citations
12582
World Ranking
3653
National Ranking
1746

Electronics and Electrical Engineering

D-Index
58
Citations
12892
World Ranking
1863
National Ranking
736

Naresh R. Shanbhag 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 Naresh R. Shanbhag 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: 446 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: 350 publications — 67th percentile

67% of scientists in this discipline score the same or lower.

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

Naresh R. Shanbhag 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 Naresh R. Shanbhag sits on this spectrum.

30 D-Index: 178 scientists 31 D-Index: 257 scientists 32 D-Index: 263 scientists 33 D-Index: 263 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: 58 D-Index — 74th percentile

74% 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

  • 2018 - Semiconductor Industry Association University Researcher Award
  • 2006 - IEEE Fellow For development of a communication-centric design paradigm for low power systems on a chip.

Overview

Naresh R. Shanbhag is affiliated with the University of Illinois at Urbana-Champaign in the United States. Their research spans multiple fields, predominantly in engineering and computer science. The main areas of study include electrical and electronic engineering, artificial intelligence, computer vision and pattern recognition, neurology, and computational theory and mathematics.

Their work focuses extensively on advanced memory and neural computing, ferroelectric and negative capacitance devices, adversarial robustness in machine learning, semiconductor materials and devices, anomaly detection techniques and applications, advanced neural network applications, and advanced image and video retrieval techniques.

Recent notable publications by Shanbhag include:

  • Benchmarking In-Memory Computing Architectures, 2022, IEEE Open Journal of the Solid-State Circuits Society
  • Comprehending In-memory Computing Trends via Proper Benchmarking, 2022, 2022 IEEE Custom Integrated Circuits Conference (CICC)

Other significant papers related to their research group or frequent collaboration partners are:

  • Deep In-Memory Architectures in SRAM: An Analog Approach to Approximate Computing, 2020, Proceedings of the IEEE
  • A 0.44-μJ/dec, 39.9-μs/dec, Recurrent Attention In-Memory Processor for Keyword Spotting, 2020, IEEE Journal of Solid-State Circuits
  • Signal Processing Methods to Enhance the Energy Efficiency of In-Memory Computing Architectures, 2021, IEEE Transactions on Signal Processing

Frequent co-authors contributing to their research include Hassan Dbouk, Saion K. Roy, Sujan K. Gonugondla, Charbel Sakr, and Ameya D. Patil.

Shanbhag has published in several venues, with a high volume of work appearing in arXiv (Cornell University), as well as multiple publications in the IEEE Journal of Solid-State Circuits, IEEE Transactions on Signal Processing, IEEE Transactions on Circuits and Systems I Regular Papers, and the IEEE Open Journal of the Solid-State Circuits Society.

The scientist has received recognition from professional organizations, including being named an IEEE Fellow in 2006 for the development of a communication-centric design paradigm for low power systems on a chip. Additionally, they were awarded the Semiconductor Industry Association University Researcher Award in 2018.

Best Publications

  • High-throughput LDPC decoders

    M.M. Mansour;N.R. Shanbhag

  • Binodal, wireless epidermal electronic systems with in-sensor analytics for neonatal intensive care

    Ha Uk Chung;Bong Hoon Kim;Jong Yoon Lee;Jungyup Lee

  • High-speed architectures for Reed-Solomon decoders

    D.V. Sarwate;N.R. Shanbhag

  • Soft digital signal processing

    R. Hegde;N.R. Shanbhag

  • Soft-Error-Rate-Analysis (SERA) Methodology

    Ming Zhang;N.R. Shanbhag

  • A 640-Mb/s 2048-bit programmable LDPC decoder chip

    M.M. Mansour;N.R. Shanbhag

  • Energy-efficient signal processing via algorithmic noise-tolerance

    Rajamohana Hegde;Naresh R. Shanbhag

  • A coding framework for low-power address and data busses

    S. Ramprasad;N.R. Shanbhag;I.N. Hajj

  • Sequential Element Design With Built-In Soft Error Resilience

    Ming Zhang;S. Mitra;T.M. Mak;N. Seifert

  • Coding for system-on-chip networks: a unified framework

    S.R. Sridhara;N.R. Shanbhag

  • Low-power VLSI decoder architectures for LDPC codes

    Mohammad M. Mansour;Naresh R. Shanbhag

  • Reliable low-power digital signal processing via reduced precision redundancy

    Byonghyo Shim;S.R. Sridhara;N.R. Shanbhag

  • A soft error rate analysis (SERA) methodology

    Ming Zhang;N. R. Shanbhag

  • A Multi-Functional In-Memory Inference Processor Using a Standard 6T SRAM Array

    Mingu Kang;Sujan K. Gonugondla;Ameya Patil;Naresh R. Shanbhag

  • A 42pJ/decision 3.12TOPS/W robust in-memory machine learning classifier with on-chip training

    Sujan Kumar Gonugondla;Mingu Kang;Naresh Shanbhag

  • Coupling-driven signal encoding scheme for low-power interface design

    Ki-Wook Kim;Kwang-Hyun-Baek;N. Shanbhag;C.L. Liu

  • Energy-efficient soft error-tolerant digital signal processing

    Byonghyo Shim;N.R. Shanbhag

  • Stochastic computation

    Naresh R. Shanbhag;Rami A. Abdallah;Rakesh Kumar;Douglas L. Jones

  • AN ENERGY-EFFICIENT VLSI ARCHITECTURE FOR PATTERN RECOGNITION VIA DEEP EMBEDDING OF COMPUTATION IN SRAM

    Mingu Kang;Min-Sun Keel;Naresh R. Shanbhag;Sean Eilert

  • Toward achieving energy efficiency in presence of deep submicron noise

    R. Hegde;N.R. Shanbhag

  • Coding for systern-on-chip networks: a unified framework

    Srinivasa R. Sridhara;Naresh R. Shanbhag

Frequent Co-Authors

Andrew C. Singer
Andrew C. Singer University of Illinois at Urbana-Champaign
Keshab K. Parhi
Keshab K. Parhi University of Minnesota
Ibrahim N. Hajj
Ibrahim N. Hajj University of Illinois at Urbana-Champaign
Douglas L. Jones
Douglas L. Jones University of Illinois at Urbana-Champaign
Byonghyo Shim
Byonghyo Shim Seoul National University
Ralf Koetter
Ralf Koetter Technical University of Munich
Lav R. Varshney
Lav R. Varshney University of Illinois at Urbana-Champaign
Sasikanth Manipatruni
Sasikanth Manipatruni Intel (United States)
Philip T. Krein
Philip T. Krein University of Illinois at Urbana-Champaign
Ian A. Young
Ian A. Young Intel (United States)

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Related Online Degrees & Career Pathways

For those interested in Electronics and Electrical Engineering, exploring related online degrees can expand career opportunities. Many professionals benefit from accelerated online degree programs for working adults, allowing them to balance education with existing job commitments and gain advanced skills faster.

In addition to technical expertise, degrees in fields like instructional design are gaining popularity. These programs equip students with the ability to create effective educational materials, a valuable skill in training and development roles within engineering firms or tech companies.

Competency-based programs offer another flexible path, especially for those with prior experience. A competency based masters degree allows students to advance by demonstrating their mastery of subject matter rather than following a traditional semester schedule.

Moreover, online education options are increasingly accommodating diverse communities. For example, there are excellent online colleges for military spouses that provide flexibility and support tailored to their unique needs, helping them pursue technical careers alongside their families.

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