World's Best Scientists 2026 revealed!

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
35
Citations
4290
World Ranking
5637
National Ranking
815

Arindam Basu 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 Arindam Basu 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: 180 publications — 23rd percentile

23% of scientists in this discipline score the same or lower.

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

Arindam Basu 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 Arindam Basu 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: 35 D-Index — 21st percentile

21% of scientists in this discipline score the same or lower.

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

Overview

Arindam Basu is affiliated with Nanyang Technological University in Singapore and specializes in research intersecting engineering and neuroscience. Their work broadly covers areas within electrical and electronic engineering, cellular and molecular neuroscience, cognitive neuroscience, artificial intelligence, and biomedical engineering.

The scientist's research focuses on advanced memory and neural computing, neuroscience and neural engineering, imaging sensors such as CCD and CMOS technologies, ferroelectric and negative capacitance devices, neural dynamics and brain function, EEG and brain-computer interfaces, as well as neural networks and reservoir computing.

Key recent publications include:

  • Halide perovskite memristors as flexible and reconfigurable physical unclonable functions, 2021, Nature Communications
  • Diffusive and Drift Halide Perovskite Memristive Barristors as Nociceptive and Synaptic Emulators for Neuromorphic Computing, 2021, Advanced Materials
  • Self healable neuromorphic memtransistor elements for decentralized sensory signal processing in robotics, 2020, Nature Communications
  • Optogenetics inspired transition metal dichalcogenide neuristors for in-memory deep recurrent neural networks, 2020, Nature Communications
  • Dendritic Computing: Branching Deeper into Machine Learning, 2021, Neuroscience

Frequent co-authors with whom Arindam Basu has collaborated extensively include:

  • Pao-Sheng Vincent Sun
  • Jyotibdha Acharya
  • Nripan Mathews
  • Sumon Kumar Bose
  • Andrés Ussa

Their research has been published repeatedly in several venues, with multiple papers appearing in:

  • arXiv (Cornell University)
  • Nature Communications
  • Neuromorphic Computing and Engineering
  • Zenodo (CERN European Organization for Nuclear Research)
  • Advanced Materials

Their work emphasizes developments in neuromorphic computing and engineering, which lie at the interface of device physics, neural science, and machine learning. This interdisciplinary approach underpins their prolific contributions to electrical engineering and neuroscience-related fields.

Best Publications

  • Synergistic Gating of Electro-Iono-Photoactive 2D Chalcogenide Neuristors: Coexistence of Hebbian and Homeostatic Synaptic Metaplasticity.

    Rohit Abraham John;Fucai Liu;Nguyen Anh Chien;Mohit R. Kulkarni

  • Halide perovskite memristors as flexible and reconfigurable physical unclonable functions.

    Rohit Abraham John;Nimesh Shah;Sujaya Kumar Vishwanath;Si En Ng

  • Ionotronic Halide Perovskite Drift-Diffusive Synapses for Low-Power Neuromorphic Computation.

    Rohit Abraham John;Natalia Yantara;Yan Fong Ng;Govind Narasimman

  • A Floating-Gate-Based Field-Programmable Analog Array

    Arindam Basu;Stephen Brink;Craig Schlottmann;Shubha Ramakrishnan

  • Deep Neural Network for Respiratory Sound Classification in Wearable Devices Enabled by Patient Specific Model Tuning

    Jyotibdha Acharya;Arindam Basu

  • Diffusive and Drift Halide Perovskite Memristive Barristors as Nociceptive and Synaptic Emulators for Neuromorphic Computing

    Rohit Abraham John;Natalia Yantara;Si En Ng;Muhammad Iszaki Bin Patdillah

  • A Learning-Enabled Neuron Array IC Based Upon Transistor Channel Models of Biological Phenomena

    S. Brink;S. Nease;P. Hasler;S. Ramakrishnan

  • Low-Power, Adaptive Neuromorphic Systems: Recent Progress and Future Directions

    Arindam Basu;Jyotibdha Acharya;Tanay Karnik;Huichu Liu

  • A 128-Channel Extreme Learning Machine-Based Neural Decoder for Brain Machine Interfaces

    Yi Chen;Enyi Yao;Arindam Basu

  • Self healable neuromorphic memtransistor elements for decentralized sensory signal processing in robotics

    Rohit Abraham John;Naveen Tiwari;Muhammad Iszaki Bin Patdillah;Mohit Rameshchandra Kulkarni

  • Neural Dynamics in Reconfigurable Silicon

    A Basu;S Ramakrishnan;C Petre;S Koziol

  • A Charge-Based Low-Power High-SNR Capacitive Sensing Interface Circuit

    Sheng-Yu Peng;M.S. Qureshi;P.E. Hasler;A. Basu

  • An Extreme Learning Machine-Based Neuromorphic Tactile Sensing System for Texture Recognition

    Mahdi Rasouli;Yi Chen;Arindam Basu;Sunil L. Kukreja

  • Nullcline-Based Design of a Silicon Neuron

    Arindam Basu;Paul E Hasler

  • Silicon spiking neurons for hardware implementation of extreme learning machines

    Arindam Basu;Sun Shuo;Hongming Zhou;Meng Hiot Lim

  • Ultralow Power Dual-Gated Subthreshold Oxide Neuristors: An Enabler for Higher Order Neuronal Temporal Correlations.

    Rohit Abraham John;Nidhi Tiwari;Chen Yaoyi;Ankit

  • A Noise Filtering Algorithm for Event-Based Asynchronous Change Detection Image Sensors on TrueNorth and Its Implementation on TrueNorth.

    Vandana Padala;Arindam Basu;Garrick Orchard

  • Optogenetics inspired transition metal dichalcogenide neuristors for in-memory deep recurrent neural networks

    Rohit Abraham John;Jyotibdha Acharya;Chao Zhu;Abhijith Surendran

  • Powering the IoT through embedded machine learning and LoRa

    Vignesh Mahalingam Suresh;Rishi Sidhu;Prateek Karkare;Aakash Patil

  • Current Mirror Array: A Novel Circuit Topology for Combining Physical Unclonable Function and Machine Learning

    Zheng Wang;Yi Chen;Aakash Patil;Jayasanker Jayabalan

  • Front-end CMOS electronics for monolithic integration with CMUT arrays: Circuit design and initial experimental results

    G. Gurun;M.S. Qureshi;M. Balantekin;R. Guldiken

  • VLSI Extreme Learning Machine: A Design Space Exploration

    Enyi Yao;Arindam Basu

  • Liquid state machine with dendritically enhanced readout for low-power, neuromorphic VLSI implementations.

    Subhrajit Roy;Amitava Banerjee;Arindam Basu

Frequent Co-Authors

Paul Hasler
Paul Hasler Georgia Institute of Technology
Chip-Hong Chang
Chip-Hong Chang Nanyang Technological University
Nripan Mathews
Nripan Mathews Nanyang Technological University
Shih-Chii Liu
Shih-Chii Liu University of Zurich
Minkyu Je
Minkyu Je Korea Advanced Institute of Science and Technology
Fahrettin Levent Degertekin
Fahrettin Levent Degertekin Georgia Institute of Technology
Abhijit Mukherjee
Abhijit Mukherjee Curtin University
Arun Majumdar
Arun Majumdar Stanford University
Nitish V. Thakor
Nitish V. Thakor National University of Singapore
Debdeep Mukhopadhyay
Debdeep Mukhopadhyay Indian Institute of Technology Kharagpur

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Related Online Degrees & Career Pathways

For students interested in Electronics and Electrical Engineering, exploring complementary online degrees can open up diverse career opportunities. For example, a project management bachelor degree online is an excellent option for those looking to lead engineering teams or handle complex technical projects efficiently.

Many professionals balancing work and study benefit from accelerated online degree programs for working adults, which offer flexible pacing and faster completion times. This approach helps you upskill quickly without putting your career on hold.

Additionally, skills in instructional design are valuable for those who want to develop training programs or technical education materials, an area closely tied to engineering education and corporate learning environments.

Finally, explore competency based universities, which focus on mastering specific skills and knowledge. These programs provide a tailored learning experience that can accelerate your expertise in key engineering competencies.

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