World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
40
Citations
7801
World Ranking
9219
National Ranking
3927

Jae-sun Seo 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 Jae-sun Seo 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: 179 publications — 38th percentile

38% of scientists in this discipline score the same or lower.

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

Jae-sun Seo 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 Jae-sun Seo 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: 40 D-Index — 37th percentile

37% of scientists in this discipline score the same or lower.

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

Overview

Jae-sun Seo is affiliated with Cornell University in the United States and has an extensive research portfolio primarily located in the fields of engineering and computer science. The scientist's work spans several specialized subfields including electrical and electronic engineering, computer vision and pattern recognition, artificial intelligence, hardware and architecture, and cellular and molecular neuroscience.

The main topics covered in Seo's research include advanced memory and neural computing, ferroelectric and negative capacitance devices, advanced neural network applications, semiconductor materials and devices, CCD and CMOS imaging sensors, domain adaptation and few-shot learning, as well as neuroscience and neural engineering.

Seo has published multiple papers in significant venues, with a high concentration of publications in journals and conferences focused on solid-state circuits and electronic devices. Frequent publication venues include:

  • IEEE Journal of Solid-State Circuits
  • arXiv (Cornell University)
  • IEEE Solid-State Circuits Letters
  • IEEE Transactions on Electron Devices
  • IEEE Journal on Emerging and Selected Topics in Circuits and Systems

Among recent publications, notable papers include:

  • "XNOR-SRAM: In-Memory Computing SRAM Macro for Binary/Ternary Deep Neural Networks," 2020, IEEE Journal of Solid-State Circuits
  • "C3SRAM: An In-Memory-Computing SRAM Macro Based on Robust Capacitive Coupling Computing Mechanism," 2020, IEEE Journal of Solid-State Circuits
  • "Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems," 2023, ACS Nano
  • "Benchmarking TinyML Systems: Challenges and Direction," 2020, arXiv (Cornell University)
  • "High-Throughput In-Memory Computing for Binary Deep Neural Networks With Monolithically Integrated RRAM and 90-nm CMOS," 2020, IEEE Transactions on Electron Devices

Seo has collaborated frequently with several co-authors, reflecting ongoing partnerships in their research areas. Frequent co-authors include:

  • Jian Meng
  • Yu Cao
  • Deliang Fan
  • Injune Yeo
  • Shihui Yin

Their research contributions have concentrated heavily on the development of memory technologies and neural computing architectures, illustrating a technical focus on hardware-related implementations and enhancements in neural network systems.

Best Publications

  • Throughput-Optimized OpenCL-based FPGA Accelerator for Large-Scale Convolutional Neural Networks

    Naveen Suda;Vikas Chandra;Ganesh Dasika;Abinash Mohanty

  • XNOR-SRAM: In-Memory Computing SRAM Macro for Binary/Ternary Deep Neural Networks

    Shihui Yin;Zhewei Jiang;Jae-Sun Seo;Mingoo Seok

  • A 45nm CMOS neuromorphic chip with a scalable architecture for learning in networks of spiking neurons

    Jae-sun Seo;Bernard Brezzo;Yong Liu;Benjamin D. Parker

  • Optimizing Loop Operation and Dataflow in FPGA Acceleration of Deep Convolutional Neural Networks

    Yufei Ma;Yu Cao;Sarma Vrudhula;Jae-sun Seo

  • Optimizing the Convolution Operation to Accelerate Deep Neural Networks on FPGA

    Yufei Ma;Yu Cao;Sarma Vrudhula;Jae-sun Seo

  • Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain

    Chetan Singh Thakur;Jamal Lottier Molin;Gert Cauwenberghs;Giacomo Indiveri

  • C3SRAM: An In-Memory-Computing SRAM Macro Based on Robust Capacitive Coupling Computing Mechanism

    Zhewei Jiang;Shihui Yin;Jae-Sun Seo;Mingoo Seok

  • XNOR-RRAM: A scalable and parallel resistive synaptic architecture for binary neural networks

    Xiaoyu Sun;Shihui Yin;Xiaochen Peng;Rui Liu

  • Scalable and modularized RTL compilation of Convolutional Neural Networks onto FPGA

    Yufei Ma;Naveen Suda;Yu Cao;Jae-sun Seo

  • Specifications of Nanoscale Devices and Circuits for Neuromorphic Computational Systems

    B. Rajendran;Yong Liu;Jae-sun Seo;K. Gopalakrishnan

  • Fully parallel write/read in resistive synaptic array for accelerating on-chip learning.

    Ligang Gao;I-Ting Wang;Pai-Yu Chen;Sarma Vrudhula

  • XNOR-SRAM: In-Memory Computing SRAM Macro for Binary/Ternary Deep Neural Networks

    Zhewei Jiang;Shihui Yin;Mingoo Seok;Jae-sun Seo

  • Mitigating Effects of Non-ideal Synaptic Device Characteristics for On-chip Learning

    Pai-Yu Chen;Binbin Lin;I-Ting Wang;Tuo-Hung Hou

  • An automatic RTL compiler for high-throughput FPGA implementation of diverse deep convolutional neural networks

    Yufei Ma;Yu Cao;Sarma Vrudhula;Jae-sun Seo

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

    Arindam Basu;Jyotibdha Acharya;Tanay Karnik;Huichu Liu

  • High-Throughput In-Memory Computing for Binary Deep Neural Networks with Monolithically Integrated RRAM and 90nm CMOS

    Shihui Yin;Xiaoyu Sun;Shimeng Yu;Jae-sun Seo

  • ALAMO: FPGA acceleration of deep learning algorithms with a modularized RTL compiler

    Yufei Ma;Naveen Suda;Yu Cao;Sarma B. K. Vrudhula

  • Technology-design co-optimization of resistive cross-point array for accelerating learning algorithms on chip

    Pai-Yu Chen;Deepak Kadetotad;Zihan Xu;Abinash Mohanty

  • Reconfigurable and customizable general-purpose circuits for neural networks

    Bernard V. Brezzo;Leland Chang;Steven K. Esser;Daniel J. Friedman

  • A Survey on the Optimization of Neural Network Accelerators for Micro-AI On-Device Inference

    Unknown

  • Monolithically Integrated RRAM- and CMOS-Based In-Memory Computing Optimizations for Efficient Deep Learning

    Shihui Yin;Jae-sun Seo;Yulhwa Kim;Xu Han

  • A 2.5 mW 80 dB DR 36 dB SNDR 22 MS/s Logarithmic Pipeline ADC

    Jongwoo Lee;J. Kang;Sunghyun Park;Jae-sun Seo

  • High-Throughput In-Memory Computing for Binary Deep Neural Networks With Monolithically Integrated RRAM and 90-nm CMOS

    Shihui Yin;Xiaoyu Sun;Shimeng Yu;Jae-Sun Seo

Frequent Co-Authors

Yu Cao
Yu Cao University of Minnesota
Shimeng Yu
Shimeng Yu Georgia Institute of Technology
Sarma Vrudhula
Sarma Vrudhula Arizona State University
Chaitali Chakrabarti
Chaitali Chakrabarti Arizona State University
Mingoo Seok
Mingoo Seok Columbia University
Dennis Sylvester
Dennis Sylvester University of Michigan–Ann Arbor
David Blaauw
David Blaauw University of Michigan–Ann Arbor
Pai-Yu Chen
Pai-Yu Chen Arizona State University
Leland Chang
Leland Chang IBM Research - Thomas J. Watson Research Center
Bipin Rajendran
Bipin Rajendran King's College London

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