Seonwoo Min
Applied Scientist, Amazon
seonwoo.min0 [AT] gmail.com
Bio
I'm an Applied Scientist at Amazon's Stores Foundational AI, starting in January 2025.
At Stores Foundational AI, I work on building Rufus—a new generative AI-powered conversational shopping experience! Before joining Stores Foundational AI, I started at Amazon in October 2023 as part of AWS HealthAI. I worked on building trustworthy NLP solutions for healthcare, including entity linking, document QA, and knowledge graph QA. Previously, during my Ph.D. (2015–2021), my research interest was primarily at the intersection of machine learning and computational biology. I was fortunate to work with talented collaborators on challenging problems, such as CRISPR gene-editing technology and training protein language models.
Before Amazon, I was a Research Scientist at the Fundamental Research Laboratory, LG AI Research, for 1.2 years. I earned my Ph.D. from the Data Science & Artificial Intelligence Laboratory at Seoul National University in 2021. I received my B.S. in Electrical and Computer Engineering from Seoul National University in 2015. I also spent two summers interning at NAVER in 2019 and 2023. I was honored to receive several awards, including the BK21 Plus Outstanding Researcher Award in 2020, the Microsoft Research Asia Fellowship Nomination Award in 2018, and the NRF Global Ph.D. Fellowship (2016–2020).
News
- [Jan 2025] I'm excited to start a new chapter at Amazon's Stores Foundational AI, working on Rufus!
- [Dec 2024] I'm thrilled to share that my research surpassed 4,000 citations with an h-index of 20 — a key career milestone!
- [Jul 2024] I'm delighted to share we've welcomed our first son into the world!
- [Oct 2023] I joined Amazon Web Services (AWS) Health AI as an Applied Scientist.
- [May 2023] I started a research internship at Hyperscalie AI Information Extraction Team, NAVER Cloud.
- [Apr 2023] One paper was accepted to Nature Methods.
- [Apr 2023] One paper was accepted to Nature Biotechnology.
- [Feb 2023] One paper was accepted to Nature Chemical Biology.
- [Jan 2023] One paper was accepted to Physiological Measurement.
- [Sep 2022] It was my last month at LG AI Research. I'm so grateful to my colleagues and excited for my next adventure!
Publications
Most recent publications on Google Scholar.
‡ indicates equal contribution.
Grounding Visual Representations With Texts for Domain Generalization
Seonwoo Min, Nokyung Park, Siwon Kim, Seunghyun Park, Jinkyu Kim
ECCV 2022: European Conference on Computer Vision
Pre-training of Deep Bidirectional Protein Sequence Representations With Structural Information
Seonwoo Min, Seunghyun Park, Siwon Kim, Hyun-Soo Choi, Sungroh Yoon
IEEE Access 2021 (50+ citations)
Also presented at NeurIPS LMRL Workshop 2019: Neural Information Processing Systems Workshop on Learning Meaningful Representations of Life
Also presented at MLCB Workshop 2019: Workshop on Machine Learrning in Computational Biology (Oral)
Deep Learning Improves Prediction of CRISPR-Cpf1 Guide RNA Activity
Hui Kwon Kim‡, Seonwoo Min‡, Myungjae Song, Soobin Jung, Jae Woo Choi, Younggwang Kim, Sangeun Lee, Sungroh Yoon, Hyongbum Kim
Nature Biotechnology 2018 (300+ citations)
Also presented at ISMB Highlights Track 2018: International Conference on Intelligent Systems for Molecular Biology Highlights Track (Oral)
Deep Learning in Bioinformatics
Seonwoo Min, Byunghan Lee, Sungroh Yoon
Briefings in Bioinformatics 2017 (1900+ citations)
Polyphonic Music Generation With Sequence Generative Adversarial Networks
Sang-gil Lee, Uiwon Hwang, Seonwoo Min, Sungroh Yoon
Journal of KIISE 2024
Contrastive Time-Series Anomaly Detection
Hyungi Kim, Siwon Kim, Seonwoo Min, Byunghan Lee
TKDE 2023: IEEE Transactions on Knowledge and Data Engineering
Massively Parallel Evaluation and Computational Prediction of the Activities and Specificities of 17 Small Cas9s
Sang-Yeon Seo, Seonwoo Min, Sungtae Lee, Jung Hwa Seo, Jinman Park, Hui Kwon Kim, Myungjae Song, Dawoon Bae, Sung-Rae Cho, Hyongbum Henry Kim
Nature Methods 2023
Deep Learning Models to Predict the Editing Efficiencies and Outcomes of Diverse Base Editors
Nahye Kim, Sungchul Choi, Sungjae Kim, Myungjae Song, Jung Hwa Seo, Seonwoo Min, Jinman Park, Sung-Rae Cho, Hyongbum Henry Kim
Nature Biotechnology 2023
Sniper2L, a High-Fidelity Cas9 Variant With High Activity
Young-hoon Kim, Nahye Kim, Ikenna Okafor, Sungchul Choi, Seonwoo Min, Joonsun Lee, Keunwoo Choi, Janice Choi, Vinayak Harihar, Youngho Kim, Jin-Soo Kim, Jungjoon K. Lee, Taekjip Ha, Hyongbum Henry Kim
Nature Chemical Biology 2023
Improving Generalization Performance of Electrocardiogram Classification Models
Hyeongrok Han, Seongjae Park, Seonwoo Min, Hyun-Soo Choi, Eunji Kim, Hyunki Kim, Sangha Park, Jinkook Kim, Junsang Park, Junho An, Kwanglo Lee, Wonsun Jeong, Sangil Chon, Kwonwoo Ha, Myungkyu Han, Sungroh Yoon
Physiological Measurement 2023
Pure Transformers Are Powerful Graph Learners
Jinwoo Kim, Dat Tien Nguyen, Seonwoo Min, Sungjun Cho, Moontae Lee, Honglak Lee, Seunghoon Hong
NeurIPS 2022: Neural Information Processing Systems (200+ citations)
Transformers Meet Stochastic Block Models
Sungjun Cho, Seonwoo Min, Jinwoo Kim, Moontae Lee, Honglak Lee, Seunghoon Hong
NeurIPS 2022: Neural Information Processing Systems
Grounding Visual Representations With Texts for Domain Generalization
Seonwoo Min, Nokyung Park, Siwon Kim, Seunghyun Park, Jinkyu Kim
ECCV 2022: European Conference on Computer Vision
TargetNet: Functional microRNA Target Prediction With Deep Neural Networks
Seonwoo Min, Byunghan Lee, Sungroh Yoon
Bioinformatics 2022
DNA Privacy: Analyzing Malicious DNA Sequences Using Deep Neural Networks
Ho Bae, Seonwoo Min, Hyun-Soo Choi, Sungroh Yoon
TCBB 2022: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Supervised Neural Discrete Universal Denoiser for Adaptive Denoising
Sungmin Cha‡, Seonwoo Min‡, Sungroh Yoon and Taesup Moon
ArXiv 2021
Generation of a More Efficient Prime Editor 2 by Addition of the Rad51 DNA-Binding Domain
Myungjae Song, Jung Min Lim, Seonwoo Min, Jeong-Seok Oh, Dong Young Kim, Jae-Sung Woo, Hiroshi Nishimasu, Sung-Rae Cho, Sungroh Yoon, Hyongbum Henry Kim
Nature Communications 2021 (50+ citations)
Towards High Generalization Performance on Electrocardiogram Classification
Hyeongrok Han, Seongjae Park, Seonwoo Min, Hyun-Soo Choi, Eunji Kim, Hyunki Kim, Sangha Park, Jinkook Kim, Junsang Park, Junho An, Kwanglo Lee, Wonsun Jeong, Sangil Chon, Kwonwoo Ha, Myungkyu Han, Sungroh Yoon
CinC 2021: Computing in Cardiology (Winner (6, 4-lead) and Runner-up (12, 3, 2-lead) in PhysioNet Challenge)
Pre-training of Deep Bidirectional Protein Sequence Representations With Structural Information
Seonwoo Min, Seunghyun Park, Siwon Kim, Hyun-Soo Choi, Sungroh Yoon
IEEE Access 2021 (50+ citations)
Also presented at NeurIPS LMRL Workshop 2019: Neural Information Processing Systems Workshop on Learning Meaningful Representations of Life
Also presented at MLCB Workshop 2019: Workshop on Machine Learrning in Computational Biology (Oral)
Protein Transfer Learning Improves Identification of Heat Shock Protein Families
Seonwoo Min, Hyungi Kim, Byunghan Lee, Sungroh Yoon
PLOS ONE 2021
Learned Embeddings From Deep Learning to Visualize and Predict Protein Sets
Christian Dallago, Konstantin Schütze, Michael Heinzinger, Tobias Olenyi, Maria Littmann, Amy X. Lu, Kevin K. Yang, Seonwoo Min, Sungroh Yoon, James T. Morton, Burkhard Rost
Current Protocols 2021 (50+ citations)
Also presented at NeurIPS LMRL Workshop 2020: Neural Information Processing Systems Workshop on Learning Meaningful Representations of Life
Also presented at ISMB BioVis Poster Track 2020: International Conference on Intelligent Systems for Molecular Biology BioVis Poster Track
Recording of Elapsed Time and Temporal Information About Biological Events Using Cas9
Jihye Park, Jung Min Lim, Seok-Jae Heo, Jinman Park, Yoojin Chang, Hui Kwon Kim, Dongmin Jung, Ji Hea Yu, Seonwoo Min, Sungroh Yoon, Sung-Rae Cho, Inkyung Jung, Taeyoung Park, Hyongbum Henry Kim
Cell 2021
Predicting the Efficiency of Prime Editing Guide RNAs in Human Cells
Hui Kwon Kim, GooSang Yu, Jinman Park, Seonwoo Min, Sungtae Lee, Sungroh Yoon, Hyongbum Henry Kim
Nature Biotechnology 2020 (200+ citations)
Prediction of the Sequence-Specific Cleavage Activity of Cas9 Variants
Nahye Kim, Hui Kwon Kim, Sungtae Lee, Jung Hwa Seo, Jae Woo Choi, Jinman Park, Seonwoo Min, Sungroh Yoon, Sung-Rae Cho, Hyongbum Henry Kim
Nature Biotechnology 2020 (100+ citations)
Bag of Tricks for Electrocardiogram Classification With Deep Neural Networks
Seonwoo Min, Hyun-Soo Choi, Hyeongrok Han, Minji Seo, Jin-Kook Kim, Junsang Park, Sunghoon Jung, Il-Young Oh, Byunghan Lee, Sungroh Yoon"
CinC 2020: Computing in Cardiology (6th Place in PhysioNet Challenge)
Sequence-Specific Prediction of the Efficiencies of Adenine and Cytosine Base Editors
Myungjae Song, Hui Kwon Kim, Sungtae Lee, Younggwang Kim, Sang-Yeon Seo, Jinman Park, Nahye Kim, Jae Woo Choi, Hyewon Jang, Jeong Hong Shin, Seonwoo Min, Jhejiu Quan, Jihun Kim, Hoon-Chul Kang, Sungroh Yoon, Hyongbum Henry Kim
Nature Biotechnology 2020 (100+ 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, Seonwoo Min, Jae Woo Choi, Tony Huang, Sungroh Yoon, David Liu, Hyongbum Henry Kim
Nature Biomedical Engineering 2020 (100+ citations)
SpCas9 Activity Prediction by DeepSpCas9, a Deep Learning-based Model With High Generalization Performance
Hui Kwon Kim, Younggwang Kim, Sungtae Lee, Seonwoo Min, Jung Yoon Bae, Jae Woo Choi, Jinman Park, Dongmin Jung, Sungroh Yoon, Hyongbum Henry Kim
Science Advances 2019 (200+ citations)
Learning-Based Instantaneous Drowsiness Detection Using Wired and Wireless EEG
Hyun-Soo Choi, Seonwoo Min, Siwon Kim, Ho Bae, Jee-Eun Yoon, Inha Hwang, Dana Oh, Chang-Ho Yun, Sungroh Yoon
IEEE Access 2019
Deep Learning Improves Prediction of CRISPR-Cpf1 Guide RNA Activity
Hui Kwon Kim‡, Seonwoo Min‡, Myungjae Song, Soobin Jung, Jae Woo Choi, Younggwang Kim, Sangeun Lee, Sungroh Yoon, Hyongbum Kim
Nature Biotechnology 2018 (300+ citations)
Also presented at ISMB Highlights Track 2018: International Conference on Intelligent Systems for Molecular Biology Highlights Track (Oral)
Deep Learning in Bioinformatics
Seonwoo Min, Byunghan Lee, Sungroh Yoon
Briefings in Bioinformatics 2017 (1900+ citations)
Deep Recurrent Neural Network-Based Identification of Precursor MicroRNAs
Seunghyun Park, Seonwoo Min, Hyun-Soo Choi, Sungroh Yoon
NeurIPS 2017: Neural Information Processing Systems (100+ citations)
Neural Universal Discrete Denoiser
Taesup Moon, Seonwoo Min, Byunghan Lee, Sungroh Yoon
NeurIPS 2016: Neural Information Processing Systems
Polyphonic Music Generation With Sequence Generative Adversarial Networks
Sang-gil Lee, Uiwon Hwang, Seonwoo Min, Sungroh Yoon
Journal of KIISE 2024
Contrastive Time-Series Anomaly Detection
Hyungi Kim, Siwon Kim, Seonwoo Min, Byunghan Lee
TKDE 2023: IEEE Transactions on Knowledge and Data Engineering
Pure Transformers Are Powerful Graph Learners
Jinwoo Kim, Dat Tien Nguyen, Seonwoo Min, Sungjun Cho, Moontae Lee, Honglak Lee, Seunghoon Hong
NeurIPS 2022: Neural Information Processing Systems (200+ citations)
Transformers Meet Stochastic Block Models
Sungjun Cho, Seonwoo Min, Jinwoo Kim, Moontae Lee, Honglak Lee, Seunghoon Hong
NeurIPS 2022: Neural Information Processing Systems
Grounding Visual Representations With Texts for Domain Generalization
Seonwoo Min, Nokyung Park, Siwon Kim, Seunghyun Park, Jinkyu Kim
ECCV 2022: European Conference on Computer Vision
Supervised Neural Discrete Universal Denoiser for Adaptive Denoising
Sungmin Cha‡, Seonwoo Min‡, Sungroh Yoon and Taesup Moon
ArXiv 2021
Pre-training of Deep Bidirectional Protein Sequence Representations With Structural Information
Seonwoo Min, Seunghyun Park, Siwon Kim, Hyun-Soo Choi, Sungroh Yoon
IEEE Access 2021 (50+ citations)
Also presented at NeurIPS LMRL Workshop 2019: Neural Information Processing Systems Workshop on Learning Meaningful Representations of Life
Also presented at MLCB Workshop 2019: Workshop on Machine Learrning in Computational Biology (Oral)
Learned Embeddings From Deep Learning to Visualize and Predict Protein Sets
Christian Dallago, Konstantin Schütze, Michael Heinzinger, Tobias Olenyi, Maria Littmann, Amy X. Lu, Kevin K. Yang, Seonwoo Min, Sungroh Yoon, James T. Morton, Burkhard Rost
Current Protocols 2021 (50+ citations)
Also presented at NeurIPS LMRL Workshop 2020: Neural Information Processing Systems Workshop on Learning Meaningful Representations of Life
Also presented at ISMB BioVis Poster Track 2020: International Conference on Intelligent Systems for Molecular Biology BioVis Poster Track
Deep Learning Improves Prediction of CRISPR-Cpf1 Guide RNA Activity
Hui Kwon Kim‡, Seonwoo Min‡, Myungjae Song, Soobin Jung, Jae Woo Choi, Younggwang Kim, Sangeun Lee, Sungroh Yoon, Hyongbum Kim
Nature Biotechnology 2018 (300+ citations)
Also presented at ISMB Highlights Track 2018: International Conference on Intelligent Systems for Molecular Biology Highlights Track (Oral)
Deep Recurrent Neural Network-Based Identification of Precursor MicroRNAs
Seunghyun Park, Seonwoo Min, Hyun-Soo Choi, Sungroh Yoon
NeurIPS 2017: Neural Information Processing Systems (100+ citations)
Neural Universal Discrete Denoiser
Taesup Moon, Seonwoo Min, Byunghan Lee, Sungroh Yoon
NeurIPS 2016: Neural Information Processing Systems
Improving Generalization Performance of Electrocardiogram Classification Models
Hyeongrok Han, Seongjae Park, Seonwoo Min, Hyun-Soo Choi, Eunji Kim, Hyunki Kim, Sangha Park, Jinkook Kim, Junsang Park, Junho An, Kwanglo Lee, Wonsun Jeong, Sangil Chon, Kwonwoo Ha, Myungkyu Han, Sungroh Yoon
Physiological Measurement 2023
TargetNet: Functional microRNA Target Prediction With Deep Neural Networks
Seonwoo Min, Byunghan Lee, Sungroh Yoon
Bioinformatics 2022
DNA Privacy: Analyzing Malicious DNA Sequences Using Deep Neural Networks
Ho Bae, Seonwoo Min, Hyun-Soo Choi, Sungroh Yoon
TCBB 2022: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Towards High Generalization Performance on Electrocardiogram Classification
Hyeongrok Han, Seongjae Park, Seonwoo Min, Hyun-Soo Choi, Eunji Kim, Hyunki Kim, Sangha Park, Jinkook Kim, Junsang Park, Junho An, Kwanglo Lee, Wonsun Jeong, Sangil Chon, Kwonwoo Ha, Myungkyu Han, Sungroh Yoon
CinC 2021: Computing in Cardiology (Winner (6, 4-lead) and Runner-up (12, 3, 2-lead) in PhysioNet Challenge)
Pre-training of Deep Bidirectional Protein Sequence Representations With Structural Information
Seonwoo Min, Seunghyun Park, Siwon Kim, Hyun-Soo Choi, Sungroh Yoon
IEEE Access 2021 (50+ citations)
Also presented at NeurIPS LMRL Workshop 2019: Neural Information Processing Systems Workshop on Learning Meaningful Representations of Life
Also presented at MLCB Workshop 2019: Workshop on Machine Learrning in Computational Biology (Oral)
Protein Transfer Learning Improves Identification of Heat Shock Protein Families
Seonwoo Min, Hyungi Kim, Byunghan Lee, Sungroh Yoon
PLOS ONE 2021
Learned Embeddings From Deep Learning to Visualize and Predict Protein Sets
Christian Dallago, Konstantin Schütze, Michael Heinzinger, Tobias Olenyi, Maria Littmann, Amy X. Lu, Kevin K. Yang, Seonwoo Min, Sungroh Yoon, James T. Morton, Burkhard Rost
Current Protocols 2021 (50+ citations)
Also presented at NeurIPS LMRL Workshop 2020: Neural Information Processing Systems Workshop on Learning Meaningful Representations of Life
Also presented at ISMB BioVis Poster Track 2020: International Conference on Intelligent Systems for Molecular Biology BioVis Poster Track
Bag of Tricks for Electrocardiogram Classification With Deep Neural Networks
Seonwoo Min, Hyun-Soo Choi, Hyeongrok Han, Minji Seo, Jin-Kook Kim, Junsang Park, Sunghoon Jung, Il-Young Oh, Byunghan Lee, Sungroh Yoon"
CinC 2020: Computing in Cardiology (6th Place in PhysioNet Challenge)
Learning-Based Instantaneous Drowsiness Detection Using Wired and Wireless EEG
Hyun-Soo Choi, Seonwoo Min, Siwon Kim, Ho Bae, Jee-Eun Yoon, Inha Hwang, Dana Oh, Chang-Ho Yun, Sungroh Yoon
IEEE Access 2019
Deep Learning in Bioinformatics
Seonwoo Min, Byunghan Lee, Sungroh Yoon
Briefings in Bioinformatics 2017 (1900+ citations)
Deep Recurrent Neural Network-Based Identification of Precursor MicroRNAs
Seunghyun Park, Seonwoo Min, Hyun-Soo Choi, Sungroh Yoon
NeurIPS 2017: Neural Information Processing Systems (100+ citations)
Massively Parallel Evaluation and Computational Prediction of the Activities and Specificities of 17 Small Cas9s
Sang-Yeon Seo, Seonwoo Min, Sungtae Lee, Jung Hwa Seo, Jinman Park, Hui Kwon Kim, Myungjae Song, Dawoon Bae, Sung-Rae Cho, Hyongbum Henry Kim
Nature Methods 2023
Deep Learning Models to Predict the Editing Efficiencies and Outcomes of Diverse Base Editors
Nahye Kim, Sungchul Choi, Sungjae Kim, Myungjae Song, Jung Hwa Seo, Seonwoo Min, Jinman Park, Sung-Rae Cho, Hyongbum Henry Kim
Nature Biotechnology 2023
Sniper2L, a High-Fidelity Cas9 Variant With High Activity
Young-hoon Kim, Nahye Kim, Ikenna Okafor, Sungchul Choi, Seonwoo Min, Joonsun Lee, Keunwoo Choi, Janice Choi, Vinayak Harihar, Youngho Kim, Jin-Soo Kim, Jungjoon K. Lee, Taekjip Ha, Hyongbum Henry Kim
Nature Chemical Biology 2023
Generation of a More Efficient Prime Editor 2 by Addition of the Rad51 DNA-Binding Domain
Myungjae Song, Jung Min Lim, Seonwoo Min, Jeong-Seok Oh, Dong Young Kim, Jae-Sung Woo, Hiroshi Nishimasu, Sung-Rae Cho, Sungroh Yoon, Hyongbum Henry Kim
Nature Communications 2021 (50+ citations)
Recording of Elapsed Time and Temporal Information About Biological Events Using Cas9
Jihye Park, Jung Min Lim, Seok-Jae Heo, Jinman Park, Yoojin Chang, Hui Kwon Kim, Dongmin Jung, Ji Hea Yu, Seonwoo Min, Sungroh Yoon, Sung-Rae Cho, Inkyung Jung, Taeyoung Park, Hyongbum Henry Kim
Cell 2021
Predicting the Efficiency of Prime Editing Guide RNAs in Human Cells
Hui Kwon Kim, GooSang Yu, Jinman Park, Seonwoo Min, Sungtae Lee, Sungroh Yoon, Hyongbum Henry Kim
Nature Biotechnology 2020 (200+ citations)
Prediction of the Sequence-Specific Cleavage Activity of Cas9 Variants
Nahye Kim, Hui Kwon Kim, Sungtae Lee, Jung Hwa Seo, Jae Woo Choi, Jinman Park, Seonwoo Min, Sungroh Yoon, Sung-Rae Cho, Hyongbum Henry Kim
Nature Biotechnology 2020 (100+ citations)
Sequence-Specific Prediction of the Efficiencies of Adenine and Cytosine Base Editors
Myungjae Song, Hui Kwon Kim, Sungtae Lee, Younggwang Kim, Sang-Yeon Seo, Jinman Park, Nahye Kim, Jae Woo Choi, Hyewon Jang, Jeong Hong Shin, Seonwoo Min, Jhejiu Quan, Jihun Kim, Hoon-Chul Kang, Sungroh Yoon, Hyongbum Henry Kim
Nature Biotechnology 2020 (100+ 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, Seonwoo Min, Jae Woo Choi, Tony Huang, Sungroh Yoon, David Liu, Hyongbum Henry Kim
Nature Biomedical Engineering 2020 (100+ citations)
SpCas9 Activity Prediction by DeepSpCas9, a Deep Learning-based Model With High Generalization Performance
Hui Kwon Kim, Younggwang Kim, Sungtae Lee, Seonwoo Min, Jung Yoon Bae, Jae Woo Choi, Jinman Park, Dongmin Jung, Sungroh Yoon, Hyongbum Henry Kim
Science Advances 2019 (200+ citations)
Deep Learning Improves Prediction of CRISPR-Cpf1 Guide RNA Activity
Hui Kwon Kim‡, Seonwoo Min‡, Myungjae Song, Soobin Jung, Jae Woo Choi, Younggwang Kim, Sangeun Lee, Sungroh Yoon, Hyongbum Kim
Nature Biotechnology 2018 (300+ citations)
Also presented at ISMB Highlights Track 2018: International Conference on Intelligent Systems for Molecular Biology Highlights Track (Oral)
Awards, Honors, and Scholarships
Awards
- [2021] Winner (6/4-lead) & Runner-up (12/3/2-lead), PhysioNet/Computing in Cardiology Challenge
- [2021] Nomination Award, Schmidt Science Fellowship
- [2020] BK21 Plus Outstanding Researcher Award, Minister of Education, South Korea
- [2019] National R&D Excellence Top 100 Award, Ministry of Science and ICT, South Korea
- [2019] Travel Award, Neural Information Processing Systems LMRL Workshop
- [2018] Research Excellence Award, Biological Research Information Center, South Korea
- [2018] Outstanding Highly-cited Paper Award, Electrical and Computer Engineering Department, SNU
- [2018] Distinguished TA Award, Innovation Center for Engineering Education, SNU
- [2018] Nomination Award, Microsoft Research Asia Fellowship
- [2017] Highly-cited Paper Paper Award, Briefings in Bioinformatics, Oxford University Press
- [2017] Travel Award, Neural Information Processing Systems
- [2015] Gold Award (Individual) & Silver Award (Team), Korea University Ski Team Association
Honors
- [2020] Chief Graduate Student, Data Science & Artificial Intelligence Laboratory, SNU
- [2019] Assistant Chief Graduate Student, Data Science & Artificial Intelligence Laboratory, SNU
- [2018] Graduate Student of the Year, Data Science & Artificial Intelligence Laboratory, SNU
- [2015] Captain, Seoul National University Ski Team
Scholarships
- [2016 - 2020] Global Ph.D. Fellowship (~100,000$), National Research Foundation of Korea
- [2018 - 2019] Global Capability Enhancement Program (~6,000$), National Research Foundation of Korea
- [2011 - 2014] National Science Scholarship (~25,000$), Korea Student Aid Foundation
Academic/Industry Activities
Mentoring
- [Jun 2024 - Oct 2024] AWS AI/ML Scholarship
Invited Talks
- [Sep 2023] Oasis of Knowledge @ Hyperscale AI, NAVER
- [Dec 2021] Open Tech Seminar @ LG AI Research
- [Jun 2018] Summer Conference @ Korean Institute of Electronics and Information Engineers
- [Jun 2018] Korea Computer Congress @ Korean Institute of Information Scientists and Engineers
- [Sep 2017] Invited Seminar @ Korea Institute of Radiology & Medical Sciences
- [Apr 2017] Bioinformatics Research Exchange @ Bioinformatics Institute of Seoul National University
- [Dec 2016] Invited Seminar @ Laboratory of Genome Editing, Yonsei University
Reviewer
- [2021 - 2025] International Conference on Learning Representations
- [2020 - 2025] International Conference on Machine Learning (Top Reviewer in 2020)
- [2019 - 2025] Neural Information Processing Systems (Top Reviewer in 2019)
- [2025] Annual AAAI Conference on Artificial Intelligence
- [2023] Journal of Chemoinformatics
- [2017 & 2021] Methods
Teaching Assistance
- [Spring 2021] Design Project for Electrical Devices & Systems @ Seoul National University
- [Fall 2019] Design Project for Electrical Devices & Systems @ Seoul National University
- [Spring 2019] Design Project for Electrical Devices & Systems @ Seoul National University
- [Fall 2018] IoT · Artificial Intelligence · Big Data (Head TA) @ Seoul National University
- [Fall 2017] Deep Learning @ Seoul National University
- [Fall 2015] Biocomputation @ Seoul National University
Curriculum Vitae
Full CV in PDF.
-
Amazon Jan 2025 -Applied Scientist
Stores Foundational AI -
Amazon Oct 2023 - Jan 2025Applied Scientist
AWS Health AI -
NAVER May 2023 - Sep 2023Research Intern
Hyperscale AI Information Extraction Team -
LG AI Research Aug 2021 - Sep 2022Research Scientist
Fundamental Research Laboratory -
Seoul National University Mar 2015 - Aug 2021
-
NAVER Jul 2019 - Aug 2019Research Intern
Optical Character Recognition Team -
Seoul National University Mar 2011 - Feb 2015B.S. Student
Electrical and Computer Engineering
Cum Laude (GPA 3.64/4.3)
Acknowledgement
This website uses the template from Prof. Martin Saveski. Thanks for sharing!