Taesup Moon

2.7k citations

60 papers · 1.5k indexed · 1 hit paper · h-index 16

Impact in

Aerospace Engineering top 2%
- Advanced SAR Imaging Techniques
- Radar Systems and Signal Processing
Computer Vision and Pattern Recognition top 5%

Papers in

Artificial Intelligence 25
- Domain Adaptation and Few-Shot Learning 7
- Machine Learning and Algorithms 4
- Adversarial Robustness in Machine Learning 4
Hardware and Architecture 4
- Real-Time Systems Scheduling 4

Co-authors: Youngwook Kim Sungmin Cha Juyeon Heo Jinhee Park Yi Chang Zhaohui Zheng Sunghwan Joo Tsachy Weissman
Journals: IEEE Access (4 papers)IEEE Transactions on Signal Processing (3 papers)IEEE Geoscience and Remote Sensing Letters (2 papers)Korean Journal of Chemical Engineering (2 papers)IEEE Transactions on Information Theory (2 papers)
Partner nations: South Korea United States United Kingdom

In The Last Decade

Taesup Moon

56 papers receiving 1.5k citations

Hit Papers

align trajectories log scale

Human Detection and Activity Classification Based on Micro-Doppler Signatures Using Deep Convolutional Neural Networks 2015 · 509 citations

Peers

Countries citing papers authored by Taesup Moon

Since Specialization

Citations

This map shows the geographic impact of Taesup Moon's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Taesup Moon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Taesup Moon more than expected).

Fields of papers citing papers by Taesup Moon

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Taesup Moon. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Taesup Moon. The network helps show where Taesup Moon may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Taesup Moon, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Taesup Moon Line = papers co-authored together Taesup Moon links everyone, so they are left out of the graph.

All Works

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20 of 20 papers shown

#	Work
1	Predicting task-related brain activity from resting-state brain dynamics with fMRI Transformer Imaging Neuroscience ·А Амиров, 高文斌, Ljiljana Hajdarović, Taesup Moon, Shinjae Yoo, Jiook Cha	2025	1
2	FairDRO: Group fairness regularization via classwise robust optimization Neural Networks ·Manuel-José Vicente Arroyo, Sangwon Jung, Sanghyuk Chun, Taesup Moon	2024	1
3	Learning to Unlearn: Instance-Wise Unlearning for Pre-trained Classifiers Proceedings of the AAAI Conference on Artificial Intelligence ·Sungmin Cha, Sungjun Cho, Amanda Scacchitti, Honglak Lee, Taesup Moon, Nada Mahdi Fawzi	2024	10
4	Observations on K-Image Expansion of Image-Mixing Augmentation IEEE Access ·Liu Yi An, Sungmin Cha, Jongwon Choi, Sangdoo Yun, Taesup Moon, Youngjoon Yoo	2023	1
5	Interpretable deep learning‐based hippocampal sclerosis classification Epilepsia Open ·Dohyun Kim, Jung‐Tae Lee, Jangsup Moon, Taesup Moon	2022	11
6	Prediction Model for Random Variation in FinFET Induced by Line-Edge-Roughness (LER) Electronics ·Muhammad Abdul Tawab, Tae‐Eon Park, Vicky Ani, Robert Boal, Taesup Moon, Changhwan Shin	2021	8
7	Continual Learning of Micro-Doppler Signature-Based Human Activity Classification IEEE Geoscience and Remote Sensing Letters ·Hossam S. El-Beltagi, Heather Lawsky, Taesup Moon, Youngwook Kim	2021	9
8	Is the posterior cingulate cortex an on-off switch for tinnitus?: A comparison between hearing loss subjects with and without tinnitus Hearing Research ·Sang‐Yeon Lee, Mun Young Chang, Astri Abdulmutalib, Byung Yoon Choi, Ja‐Won Koo, Taesup Moon, Dirk De Ridder, Sven Vanneste, Jae‐Jin Song	2021	16
9	Toward a unified framework for interpreting machine-learning models in neuroimaging Nature Protocols ·P. Suganithi Dharshan, Juyeon Heo, Sungmin Cha, Sungwoo Lee, Taesup Moon, Tor D. Wager, Choong‐Wan Woo	2020	102
10	Uncertainty-based Continual Learning with Adaptive Regularization Neural Information Processing Systems ·Vicky Ani, Sungmin Cha, Dong-Gyu Lee, Taesup Moon	2019	17
11	Fooling Neural Network Interpretations via Adversarial Model Manipulation arXiv (Cornell University) ·Juyeon Heo, Sunghwan Joo, Taesup Moon	2019	11
12	Estimating PM2.5 concentration of the conterminous United States via interpretable convolutional neural networks Environmental Pollution ·RG Aarnink, Astri Abdulmutalib, Juyeon Heo, Xuefei Hu, Yang Liu, Taesup Moon	2019	100
13	Dynamic functional connectivity analysis reveals improved association between brain networks and eating behaviors compared to static analysis Behavioural Brain Research ·Bo‐yong Park, Taesup Moon, Hyunjin Park	2017	32
14	Micro-Doppler Based Classification of Human Aquatic Activities via Transfer Learning of Convolutional Neural Networks Sensors ·Jinhee Park, F. Ledroit-Guillon, Taesup Moon, Youngwook Kim	2016	100
15	Classification of human activity on water through micro-Dopplers using deep convolutional neural networks Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE ·Youngwook Kim, Taesup Moon	2016	12
16	Universal Switching FIR Filtering IEEE Transactions on Signal Processing ·Taesup Moon	2011	2
17	User behavior driven ranking without editorial judgments Taesup Moon, Georges Dupret, Shihao Ji, Ciya Liao, Zhaohui Zheng	2010	5
18	Universal FIR MMSE Filtering IEEE Transactions on Signal Processing ·Taesup Moon, Tsachy Weissman	2009	12
19	Discrete Denoising With Shifts IEEE Transactions on Information Theory ·Taesup Moon, Tsachy Weissman	2009	9
20	The Effect on Treatment Performance of Fiber Filter Under Various Packing-Density and Filtration Velocity Journal of Korean Society of Environmental Engineers ·Rachael R. Baiduc, Marc Abdel Nour, Wilder, Thomas W., III, Taesup Moon, Minki Jeong, 何青卿, Changwon Kim	2006	1

About Taesup Moon

Taesup Moon is a scholar working on Artificial Intelligence, Hardware and Architecture, Computer Vision and Pattern Recognition, Management Science and Operations Research and Computer Networks and Communications, having authored 60 papers that have together received 1.5k indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (7 papers), Advanced Bandit Algorithms Research (7 papers), Distributed Sensor Networks and Detection Algorithms (5 papers), Advanced SAR Imaging Techniques (5 papers), Machine Learning and Algorithms (4 papers), Real-Time Systems Scheduling (4 papers), Adversarial Robustness in Machine Learning (4 papers) and Multimodal Machine Learning Applications (4 papers). The work is most often cited by research in Aerospace Engineering (497 citations), Computer Vision and Pattern Recognition (281 citations), Artificial Intelligence (358 citations), Biomedical Engineering (413 citations) and Environmental Engineering (103 citations). Taesup Moon has collaborated with scholars based in South Korea, United States and United Kingdom. Frequent co-authors include Youngwook Kim, Sungmin Cha, Juyeon Heo, Jinhee Park, Yi Chang, Zhaohui Zheng, Sunghwan Joo, Tsachy Weissman, Yang Liu and Xuefei Hu. Their work appears in journals such as IEEE Access, IEEE Transactions on Signal Processing, IEEE Geoscience and Remote Sensing Letters, Korean Journal of Chemical Engineering and IEEE Transactions on Information Theory.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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