2026 Sungroh Yoon: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	59	3426	3330	18	18	321	13808

Overview

Sungroh Yoon is affiliated with Seoul National University in South Korea and has an extensive publication record in the field of Computer Science, with a focus on Artificial Intelligence and Computer Vision and Pattern Recognition. Their research spans a variety of subfields and topics related to modern machine learning techniques and applications.

The main research topics covered in Sungroh Yoon's work include:

Advanced Neural Network Applications
Topic Modeling
Adversarial Robustness in Machine Learning
Domain Adaptation and Few-Shot Learning
Generative Adversarial Networks and Image Synthesis
Anomaly Detection Techniques and Applications
Machine Learning in Materials Science

Frequent publication venues where Sungroh Yoon's work appears are:

arXiv (Cornell University)
IEEE Access
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Proceedings of the AAAI Conference on Artificial Intelligence
Nature Biotechnology

Examples of recent papers authored or co-authored by Sungroh Yoon include:

ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines, 2021, IEEE Access
Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection, 2020, Proceedings of the AAAI Conference on Artificial Intelligence
Predicting the efficiency of prime editing guide RNAs in human cells, 2020, Nature Biotechnology
Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search, 2020, arXiv (Cornell University)

Sungroh Yoon collaborates frequently with several researchers, including:

Dahuin Jung
Seonwoo Min
Jooyoung Choi
Jisoo Mok
Siwon Kim

The scientist has contributed to book publications, notably with Elsevier BV, publishing the book Deep Learning in Bioinformatics in 2022.

Best Publications

Deep learning in bioinformatics

Seonwoo Min;Byunghan Lee;Sungroh Yoon

1672
Citations
ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models

Jooyoung Choi;Sungwon Kim;Yonghyun Jeong;Youngjune Gwon

546
Citations
Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines

Kukjin Choi;Jihun Yi;Changhwa Park;Sungroh Yoon

492
Citations
Patch SVDD: Patch-Level SVDD for Anomaly Detection and Segmentation

Jihun Yi;Sungroh Yoon

478
Citations
FickleNet: Weakly and Semi-Supervised Semantic Image Segmentation Using Stochastic Inference

Jungbeom Lee;Eunji Kim;Sungmin Lee;Jangho Lee

459
Citations
Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection

Sei Joon Kim;Seongsik Park;Byunggook Na;Sungroh Yoon

434
Citations
Deep learning improves prediction of CRISPR–Cpf1 guide RNA activity

Hui Kwon Kim;Seonwoo Min;Myungjae Song;Myungjae Song;Soobin Jung

372
Citations
RNA design rules from a massive open laboratory

Jeehyung Lee;Wipapat Kladwang;Minjae Lee;Daniel Cantu

361
Citations
How Generative Adversarial Networks and Their Variants Work: An Overview

Yongjun Hong;Uiwon Hwang;Jaeyoon Yoo;Sungroh Yoon

329
Citations
Got target? Computational methods for microRNA target prediction and their extension.

Hyeyoung Min;Sungroh Yoon

262
Citations
Predicting the efficiency of prime editing guide RNAs in human cells.

Hui Kwon Kim;Goosang Yu;Jinman Park;Seonwoo Min

259
Citations
Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search

Jaehyeon Kim;Sungwon Kim;Jungil Kong;Sungroh Yoon

225
Citations
SpCas9 activity prediction by DeepSpCas9, a deep learning-based model with high generalization performance.

Hui Kwon Kim;Younggwang Kim;Sungtae Lee;Seonwoo Min

224
Citations
Comprehensive ensemble in QSAR prediction for drug discovery.

Sunyoung Kwon;Sunyoung Kwon;Ho Bae;Jeonghee Jo;Sungroh Yoon

220
Citations
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation

Jungbeom Lee;Eunji Kim;Sungroh Yoon

208
Citations
Prediction of regulatory modules comprising microRNAs and target genes

Sungroh Yoon;Giovanni De Micheli

190
Citations
Prediction of the sequence-specific cleavage activity of Cas9 variants

Nahye Kim;Hui Kwon Kim;Sungtae Lee;Jung Hwa Seo

189
Citations
BBAM: Bounding Box Attribution Map for Weakly Supervised Semantic and Instance Segmentation

Jungbeom Lee;Jihun Yi;Chaehun Shin;Sungroh Yoon

170
Citations
Towards a Rigorous Evaluation of Time-series Anomaly Detection.

Siwon Kim;Kukjin Choi;Hyun-Soo Choi;Byunghan Lee

138
Citations
High-throughput analysis of the activities of xCas9, SpCas9-NG and SpCas9 at matched and mismatched target sequences in human cells.

Hui Kwon Kim;Sungtae Lee;Younggwang Kim;Jinman Park

132
Citations
Big/little deep neural network for ultra low power inference

Eunhyeok Park;Dongyoung Kim;Soobeom Kim;Yong-Deok Kim

87
Citations
How Generative Adversarial Networks and Their Variants Work: An Overview of GAN

Yongjun Hong;Uiwon Hwang;Jaeyoon Yoo;Sungroh Yoon

1
Citations

Frequent Co-Authors

Jongmoo Choi University of Southern California

Luca Benini ETH Zurich

G. De Micheli École Polytechnique Fédérale de Lausanne

Hyun Jae Kim Yonsei University

Rhiju Das Stanford University

Tsachy Weissman Stanford University

David R. Liu Broad Institute

Enrico Macii Polytechnic University of Turin

Young-Han Kim University of California, San Diego

Jongsik Chun Seoul National University

External Links

Personal website of Sungroh Yoon Google Scholar page

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