Sungchul Kim
Senior Research Scientist
Adobe
San Jose, CA, 95110, United States
www.adobe.com
I am a Senior Research Scientist at Adobe Research, holding a Ph.D. in Computer Science and Engineering from the Pohang University of Science and Technology (POSTECH), which I earned in the summer of 2015. Under the guidance of Prof. Hwanjo Yu, my doctoral research focused on developing mutation profiles for the top-k patient search and stratifying cancer subtypes. My expertise lies in data representation and analysis, employing various data mining techniques such as regression, ranking, and dimensionality reduction. More recently, my work has pivoted towards generative AI in the context of industrial analytics tools.
Education & Career
2016.07 - Present: Research Scientist at Adobe Research
2016.02 - 2016.05: Data Analyst at Bagelcode
2015.08 - 2015.12: Intern at Adobe Research (Systems Technology Lab, mentor: Eunyee Koh)
2008.08 - 2015.08: PhD in Computer Science and Engineering, POSTECH
2011.06 - 2011.08: Intern at Microsoft Research Redmond (NLP group, mentor: Kristina Toutanova)
2010.09 - 2011.05: Intern at Microsoft Research Asia (Online Advertising team, mentor: Tie-Yan Liu and Tao Qin) [Excellent intern certificate]
2008.02: B.S. in Computer Science and Engineering, POSTECH
Research Interest
Digital Marketing: Predictive analysis and user behavior analysis
Supervised Learning: Regression and ranking
Online Advertising with text mining
Recommender systems
Articles
Adobe's new custom AI assistant lets users explore data - https://research.adobe.com/news/adobes-new-custom-ai-assistant-lets-user-explore-data-just-by-asking-in-their-own-words/
Customer Journey Analytics with Natural Language Queries - https://news.adobe.com/news/news-details/2023/Adobe-Adds-Powerful-New-Sensei-GenAI-Services-Across-Experience-Cloud-To-Accelerate-Profitable-Growth-for-Enterprises-default.aspx/default.aspx
Adobe Experience Platform Identity Graph is the Foundation for the Unified Profile (https://medium.com/adobetech/adobe-experience-platform-identity-graph-is-the-foundation-for-the-unified-profile-e8435d26dce7, Contributed to the probabilistic algorithm)
Adobe: How It's Unleashing AI (https://www.forbes.com/sites/tomtaulli/2019/03/27/adobe-how-its-unleashing-ai , Contributed to the prediction model)
Publication
2024
Tackling Long-Tailed Entities for Temporal Knowledge Graph Completion, Mehrnoosh Mirtaheri, Ryan Rossi, Sungchul Kim, Kanak Mahadik, Tong Yu, Xiang Chen and Mohammad Rostami, TheWebConference'24 (short paper)
SciCapenter: Supporting Caption Composition for Scientific Figures with Machine-Generated Captions and Ratings, Ting-Yao Hsu, Chieh-Yang Huang, Shih-Hong Huang, Ryan Rossi, Sungchul Kim, Tong Yu, Professor C Lee Giles, Dr. Ting-Hao Kenneth Huang, CHI 2024 Late-Breaking Work
Fairness-Aware Graph Neural Networks: A Survey April Chen, Ryan A. Rossi, Namyong Park, Puja Trivedi, Yu Wang, Tong Yu, Sungchul Kim, Franck Dernoncourt, Nesreen K. Ahmed, TKDD
Evolving Super Graph Neural Networks for Large-scale Time-Series Forecasting, Hongjia Chen, Ryan Rossi, Kanak Mahadik, Sungchul Kim, Hoda Eldardiry, PAKDD'24
Convergence-Aware Online Model Selection with Time-Increasing Bandits, Yu Xia, Fang Kong, Tong Yu, Liya Guo, Ryan Rossi, Sungchul Kim, Shuai Li, TheWebConference'24
2023
GPT-4 as an Effective Zero-Shot Evaluator for Scientific Figure Captions, Ting-Yao Hsu, Chieh-Yang Huang, Ryan Rossi, Sungchul Kim, C. Giles, Ting-Hao Huang, EMNLP'23-Findings
Hypergraph Neural Networks for Time-series Forecasting, Hongjie Chen, Ryan Rossi, Kanak Mahadik, Sungchul Kim, and Hoda Eldardiry, BigData 2023
Interpretable Unsupervised Log Anomaly Detection, Jaeho Bang, Sungchul Kim, Ryan Rossi, Tong Yu, and Handong Zhao, BigData 2023 (Extended Abstract papers)
Summaries as Captions: Generating Figure Captions for Scientific Documents with Automated Text Summarization. Chieh-Yang Huang, Ting-Yao Hsu, Ryan Rossi, Ani Nenkova, Sungchul Kim, Gromit Yeuk-Yin Chan, Eunyee Koh, C Lee Giles and Ting-Hao Huang, ILNG 2023 [Awarded Best Paper]
User-Regulation Deconfounded Conversational Recommender System with Bandit Feedback, Yu Xia, Junda Wu, Tong Yu, Sungchul Kim, Ryan A. Rossi, and Shuai Li, KDD 2023
Federated Domain Adaptation for Named Entity Recognition via Distilling with Heterogeneous Tag Sets, Rui Wang, Tong Yu, Junda Wu, Handong Zhao, Sungchul Kim, Ruiyi Zhang, Subrata Mitra, and Ricardo Henao, ACL 2023
Direct Embedding of Temporal Network Edges via Time-Decayed Line Graphs, Sudhanshu Chanpuriya, Ryan A. Rossi, Sungchul Kim, Tong Yu, Jane Hoffswell, Nedim Lipka, Shunan Guo, and Cameron Musco, International Conference on Learning Representations (ICLR) 2023 (paper)
2022
AutoForecast: Automatic Time-Series Forecasting Model Selection, Mustafa Abdallah, Ryan Rossi, Kanak Mahadik, Sungchul Kim, Handong Zhao and Saurabh Bagchi, CIKM 2022 short paper
Implicit Session Contexts for Next-Item Recommendations, Sejoon Oh, Ankur Bharadwaj, Jongseok Han, Sungchul Kim, Ryan Rossi and Srijan Kumar, CIKM 2022 short paper
AutoMARS: Searching to Compress Multi-Modality Recommendation Systems, Duc Hoang, Haotao Wang, Handong Zhao, Ryan Rossi, Sungchul Kim, Kanak Mahadik and Zhangyang Wang, CIKM 2022 short paper
Bundle MCR: Towards Conversational Bundle Recommendation, Zhankui He, Handong Zhao, Tong Y, Sungchul Kim, Fan Du, Julian McAuley, RecSys 2022
Graph Deep Factors for Probabilistic Time-series Forecasting, Hongjie Chen, Ryan A. Rossi, Kanak Mahadik, Sungchul Kim, Hoda Eldardiry, TKDD
External Knowledge Infusion for Tabular Pre-training Models with Dual-adapters, Can Qin, Sungchul Kim, Handong Zhao, Tong Yu, Ryan Rossi, Yun Fu, KDD 2022
Few-Shot Class-Incremental Learning for Named Entity Recognition, Rui Wang, Tong Yu, Handong Zhao, Sungchul Kim, Subrata Mitra, Ruiyi Zhang, Ricardo Henao, ACL 2022
Personalized Visualization Recommendation, Xin Qian, Ryan A. Rossi, Fan Du, Sungchul Kim, Eunyee Koh, Sana Malik, Tak Yeon Lee, Nesreen K. Ahmed, ACM Transactions on the Web (TWEB)
On Generalizing Static Node Embedding to Dynamic Settings, Di Jin, Sungchul Kim, Ryan A. Rossi, Danai Koutra, WSDM 2022
CGC: Contrastive Graph Clustering for Community Detection and Tracking, Namyong Park, Ryan Rossi, Eunyee Koh, Iftikhar Ahamath Burhanuddin, Sungchul Kim, Fan Du, Nesreen Ahmed and Christos Faloutsos, The Web Conference (WWW) 2022
VisGNN: Personalized Visualization Recommendation via Graph Neural Networks, Fayokemi Ojo, Ryan Rossi, Jane Hoffswell, Shunan Guo, Fan Du, Sungchul Kim, Chang Xiao and Eunyee Koh, The Web Conference (WWW) 2022
2021
Influence-guided Data Augmentation for Neural Tensor Completion, Sejoon Oh, Sungchul Kim, Ryan Rossi, Srijan Kumar, CIKM'21
From Closing Triangles to Higher-Order Motif Closures for Better Unsupervised Online Link Prediction, Ryan Rossi, Anup Rao, Sungchul Kim, Eunyee Koh, Nesreen K. Ahmed, Gang Wu, CIKM'21
EXACTA: Explainable Column Annotation, Yikun Xian, Handong Zhao, Tak Yeon Lee, Sungchul Kim, Ryan A. Rossi , Zuohui Fu, Gerard de Melo, and S. Muthukrishnan, KDD 2021
Learning to Recommend Visualizations from Data, Xin Qian, Ryan A. Rossi, Fan Du, Sungchul Kim, Eunyee Koh , Sana Malik, Tak Yeon Lee, and Joel Chan, KDD 2021
Graph Deep Factor Model for Cloud Utilization Forecasting, Hongjie Chen, Ryan A Rossi, Kanak Mahadik, Sungchul Kim (Adobe), and Hoda Eldardiry, KDD 2021
EDGE: Enriching Knowledge Graph Embeddings with External Text, Saed Rezayi, Handong Zhao, Sungchul Kim, Ryan A. Rossi, Nedim Lipka, and Sheng Li, NAACL 2021
Learning Contextualized Knowledge Structures for Commonsense Reasoning, Jun Yan, Mrigank Raman, Aaron Chan, Tianyu Zhang, Ryan Rossi, Handong Zhao, Sungchul Kim, Nedim Lipka and Xiang Ren, ACL-IJCNLP 2021
Generating Accurate Caption Units For Figure Captioning, Xin Qian, Eunyee Koh, Fan Du, Sungchul Kim, Joel Chan, Ryan Rossi, Sana Malik and Tak Yeon Lee, Proceedings of The Web Conference (WWW) 2021
Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation, Mrigank Raman, Hansen Wang, PeiFeng Wang, Siddhant Agarwal, Sungchul Kim, Ryan Rossi, Handong Zhao, Nedim Lipka, Xiang Ren, ICLR'21
2020
Learning Contextualized knowledge Structures for Commonsense Reasoning, Jun Yan, Mrigank Raman, Tianyu Zhang, Ryan Rossi, Handong Zhao, Sungchul Kim, Nedim Lipka, Xiang Ren, arXiv:2010.12873 (short version in KR2ML@NeurIPS 2020) [paper]
On Proximity and Structural Role-based Embeddings in Networks: Misconceptions, Techniques, and Applications, Ryan A. Rossi, Di Jin, Sungchul Kim, Nesreen K. Ahmed, Danai Koutra, John Boaz Lee, Transactions on Knowledge Discovery from Data (TKDD), Pages 19, 2020.
Heterogeneous Graphlets, Ryan A. Rossi, Nesreen K. Ahmed, Aldo Carranza, David Arbour, Anup Rao, Sungchul Kim, Eunyee Koh, Transactions on Knowledge Discovery from Data (TKDD), Pages 43, 2020.
Interactive Event Sequence Prediction for Marketing Analysts, Fan Du, Shunan Guo, Sana Malik, Eunyee Koh, Sungchul Kim, Zhicheng Liu, CHI Extended Abstracts on Human Factors in Computing Systems, 2020
A Formative Study on Designing Accurate and Natural Figure Captioning Systems, Xin Qian, Eunyee Koh, Fan Du, Sungchul Kim, Joel Chan, CHI Extended Abstracts on Human Factors in Computing Systems, 2020
Fast Hierarchical Graph Clustering in Linear-Time, Ryan A. Rossi, Nesreen K. Ahmed, Eunyee Koh, and Sungchul Kim, Proceedings of The Web Conference (WWW) 2020
From Closing Triangles to Closing Higher-Order Motifs, Ryan A. Rossi, Anup Rao, Sungchul Kim, Eunyee Koh, and Nesreen K. Ahmed Proceedings of The Web Conference (WWW) 2020
A Structural Graph Representation Learning Framework, Ryan Rossi, Nesreen Ahmed, Eunyee Koh, Sungchul Kim, Anup Rao and Yasin Abbasi-Yadkori, WSDM (acceptance rate: 15%), 2020
Figure Captioning with Reasoning and Sequence-Level Training, Charles Chen, Ruiyi Zhang, Eunyee Koh, Sungchul Kim, Scott Cohen, Ryan Rossi, Winter Conference on Applications of Computer Vision (WACV), 2020.
2019
Attention Models in Graphs: A Survey, John Boaz Lee, Ryan A. Rossi, Sungchul Kim, Nesreen K. Ahmed, Eunyee Koh, Transactions on Knowledge Discovery from Data (TKDD), Pages 19, 2019.
Graph Convolutional Networks with Motif-based Attention, John Boaz Lee, Ryan Rossi, Xiangnan Kong, Sungchul Kim, Eunyee Koh, Anup Rao, CIKM, 2019
Heterogeneous Graphlets, Ryan A. Rossi, Nesreen K. Ahmed, Aldo Carranza, David Arbour, Anup Rao, Sungchul Kim, Eunyee Koh, MLG KDD, Pages 8, 2019.
Towards Robust and Discriminative Sequential Data Learning: When and How to Perform Adversarial Training?, Xiaowei Jia, Sheng Li, Handong Zhao, Sungchul Kim and Vipin Kumar, KDD, 2019
Latent Network Summarization, Di Jin, Ryan Rossi, Danai Koutra, Eunyee Koh, Sungchul Kim and Anup Rao, KDD, 2019
Visualizing Uncertainty and alternatives in Event Sequence Predictions, Shunan Guo, Fan Du, Sana Malik, Eunyee Koh, Sungchul Kim, Zhicheng Liu, Donghyun Kim, Hongyuan Zha, and Nan Cao, CHI, 2019
Domain Switch-Aware Holistic Recurrent Neural Network for Modeling Multi-Domain User Behavior, Donghyun Kim, Sungchul Kim, Handong Zhao, Sheng Li, Ryan Rossi, and Eunyee Koh, WSDM (acceptance rate: 16%), 2019
2018
Conversion Prediction from Clickstream: Modeling Market Prediction and Customer Predictability, Jinyoung Yeo, Seung-won Hwang, Sungchul Kim, Eunyee Koh, Nedim Lipka, Transactions on Knowledge and Data Engineering (TKDE), 2018
Dynamic Network Embeddings: From Random Walks to Temporal Random Walks, Giang Nguyen, John Boaz Lee, Ryan Rossi, Nesreen Ahmed, Eunyee Koh, and Sungchul Kim, IEEE BigData, 2018
Predictive Analysis by Leveraging Temporal User Behavior, Charles Chen, Sungchul Kim, Hung Bui, Ryan Rossi, Branislav Kveton, Eunyee Koh and Razvan Bunescu, CIKM (industrial track, acceptance rate: 26%), 2018
Perceptual Similarity Ranking of Temporal Heatmaps Using Convolutional Neural Networks, Sana Malik, Sungchul Kim and Eunyee Koh, EE-USAD, 2018
Continuous-Time Dynamic Network Embeddings, Giang Hoang Nguyen, John Boaz Lee, Ryan A. Rossi, Nesreen K. Ahmed, Eunyee Koh, Sungchul Kim, WWW BigNet, 2018
Older
WimNet: Vision Search for Web Logs, Sungchul Kim, Sana Malik, Nedim Lipka, and Eunyee Koh, WWW (poster), 2017
Probabilistic Visitor Stitching on Cross-device Web Logs, Sungchul Kim, Nikhil Kini, Jay Pujara, Lise Getoor, Eunyee Koh, WWW (acceptance rate: 17%), 2017
Predicting Online Purchase Conversion for Retargeting, Jinyoung Yeo, Sungchul Kim, Eunyee Koh, Seung-won Hwnag, and Nedim Lipka, WSDM, 2017
Browsing2purchase: Online Customer Model for Sales Forecasting in an E-Commerce Site, Jinyoung Yeo, Sungchul Kim, Eunyee Koh, Seung-Won Hwang and Nedim Lipka, WWW (poster), 2016
Purchase Intention Mining by Leveraging Item-Item Relationship, Sungchul Kim, Jinyoung Yeo, Eunyee Koh and Nedim Lipka, WWW (poster), 2016
Tumor Stratification with Four Somatic Mutation Profiles, Sungchul Kim, Lee Sael, Hwanjo Yu, ISMB/ECCB, 2015
Spoiler Detection in TV Program Tweets, Sungho Jeon, Sungchul Kim, Hwanjo Yu, Information Sciences (SCI), 2015
A Mutation Profile for Top-k Patient Search Exploiting Gene-Ontology and Orthogonal Non-negative Matrix Factorization, Sungchul Kim, Lee Sael, Hwanjo Yu, Bioinformatics (SCI), 2015
Identifying cancer subtypes based on somatic mutation profile, Sungchul Kim, Lee Sael, Hwanjo Yu, DTMBIO, 2014
When to recommend: A new issue on TV show recommendation, Jinoh Oh, Sungchul Kim, Jinha Kim, Hwanjo Yu, Information Sciences (SCI), 2014.10
Processing time-dependent shortest path queries without pre-computed speed information on road networks, Jinha Kim, Wook-Shin Han, Jinoh Oh, Sungchul Kim, Hwanjo Yu, Information Sciences (SCI), 2014.10
Advertiser-Centric Approach to Understand User Click Behavior in Sponsored Search, Sungchul Kim, Hwanjo Yu, Tao Qi, Tie-Yan Liu, Information Science (SCI), 2014
LMDS-based Approach for Efficient Top-k Local Ligand-Binding Site Search, Sungchul Kim, Lee Sael, Hwanjo Yu, International Journal of Data Mining and Bioinformatics (SCI-E), 2014
Efficient Protein Structure Search using Indexing Methods, Sungchul Kim, Lee Sael, Hwanjo Yu, BMC Medical Informatics and Decision Making (SCI-E), 2013
Efficient Local ligand-binding site search using Landmark MDS, Sungchul Kim, Lee Sael, Hwanjo Yu, DTMBIO, 2013
Don’t be Spoiled by Your Friends: Spoiler Detection in TV Program Tweets, Sungho Jeon, Sungchul Kim, Hwanjo Yu, ICWSM, 2013
Indexing Methods for Efficient Protein 3D Surface Search, Sungchul Kim, Lee Sael, Hwanjo Yu, DTMBIO, 2012
Finding Core Topics: Topic Extraction with Clustering on Tweet, Sungchul Kim, Sungho Jeon, Jinha Kim, Young-Ho Park, Hwanjo Yu, SNSDB, 2012
Multilingual Named Entity Recognition using Parallel Data and Metadata from Wikipedia, Sungchul Kim, Kristina Toutanova, and Hwanjo Yu, ACL, 2012
Advertiser-Centric Approach to Understand User Click Behavior in Sponsored Search, Sungchul Kim, Hwanjo Yu, Tao Qi, Tie-Yan Liu, CIKM, 2011
Passive Sampling for Regression, Hwanjo Yu and Sungchul Kim, ICDM, 2010
RankSVR: Can Preference Data Help Regression?, Hwanjo Yu, Sungchul Kim, and Seung-Hoon Na, CIKM, 2010
Enabling Multi-Level Relevance Feedback on PubMed by Integrating Rank Learning into DBMS, Hwanjo yu, Taehoon Kim, Jinoh Oh, Ilhwan Ko, Sungchul Kim, WookShin Han, BMC Bioinformatics (SCI), 2010
SVM tutorial - Classificaion, regression and Ranking, Hwanjo Yu, Sungchul Kim, Handbook of Natural Computing Springer, 2010
VRIFA: A Nonlinear SVM Visualization Tool using Nomogram and Localized Radial Basis Function (LRBF) Kernels, Ngo Anh Vien, Nguyen Hoang Viet, TaeChoong Chung, Hwanjo Yu, Sungchul Kim, Baek Hwan Cho, CIKM, 2009
RefMed: Relevance Feedback Retrieval System fo PubMed, Hwanjo Yu, Taehoon Kim, Jinoh Oh, Ilhwan Kim), Sungchul Kim, CIKM, 2009