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Digital Health Intelligence Systems Lab
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Mogan Gim, Ph.D

 Hello I'm Mogan Gim who is currently a Assistant Professor in Hankuk University of Foreign Studies, Department of Biomedical Engineering (BME). I earned my Ph.D. in Computer Science from Korea University under the guidance of Professor Kang, who served as my principal investigator.   During my Ph.D program under Professor Kang's supervision, I took part in various collaborative projects including SK pucore's SMARTUS development project and Sony AI's Gastronomy Project.  

My title for my doctoral dissertation is Deep Learning Architectures using Set-Oriented Data Representations . As the title says, my research interests revolve around solving real-world problems using set-oriented data representations. I primarily tackle such problems by devising a deep learning-based approach that addresses their domain-specific aspects. I also focus on developing novel deep learning architectures that best utilizes those aspects for not only enhancing their task performance but also model explainability.

 During my Ph.D program, I have engaged in several applied data science projects of  diverse domains including Food Informatics, Drug Discovery, Material Science and so on. I enjoy learning not only the advancements of cutting-edge deep learning methodologies  but also unexplored fields of knowledge. Though I recieved my Bachelor's Degree in Computer Science, I have seldom restrained myself from being intrigued in other fields of science such as organic chemistry, molecular biology, culinary studies, food nutrition as on. I believe it is fascinating to infuse such knowledge in developing AI-driven systems that can contribute to society. These academic characteristics are the key ingredients in my previous publications and current research proejcts.

Currently, my central research interest is Digital Health, a multi-dicsiplinary applied data science domain that spans Bioinformatics, Cheminformatics and Food Informatics. My utlimate goal is to pursue my research towards promoting social health through development of a AI-driven Drug Discovery Pipeleine and Precision Nutrition System.

 [CV] [Email] [Scholar] [Github] [LinkedIn] [ORCID] [Office Location (Room 513)]

Selected Publications

  • Hajung Kim*, Mogan Gim*, Seungheun Baek, Soyon Park, Sunkyu Kim**, Jaewoo Kang**, "BADGER: biologically-aware interpretable differential gene expression ranking model",  Bioinformatics Advances, vol. 5, https://doi.org/10.1093/bioadv/vbaf029

  • Seungheun Baek*, Soyon Park*, Yan Ting Chok, Mogan Gim**, Jaewoo Kang**, "GPO-VAE: Modeling Explainable Gene Perturbation Responses utilizing GRN-Aligned Parameter Optimization", ISMB-ECCB 2025, Jul 20--24, Liverpool, UK (Full Pape Accepted, In Press)  https://arxiv.org/abs/2501.18973

  • Seungheun Baek*, Soyon Park*, Yan Ting Chok, Junhyun Lee, Jueon Park, Mogan Gim**, Jaewoo Kang**, "Cradle-VAE: Enhancing Single-Cell Gene Perturbation Modeling with Counterfactual Reasoning-based Artifact Disentanglement", AAAI 2025, Feb 25--Mar 4, Philadelphia, USA (Full Paper Accepted), https://doi.org/10.1609/aaai.v39i15.33695 

  • Mogan Gim*, Jueon Park*, Soyon Park, Sanghoon Lee, Seungheun Baek, Junhyun Lee, Ngoc-Quang Nguyen and Jaewoo Kang. "MolPLA: A Molecular Pretraining Framework for Learning Cores, R-Groups and their Linker Joints", ISMB 2024, July 12--16, 2024, Montreal, Canada (Full Paper Accepted, Oral Presentation, Bioinformatics, vol. 40, https://doi.org/10.1093/bioinformatics/btae256)

  • Mogan Gim, Junseok Choi, Seungheun Baek, Jueon Park, Chaeeun Lee, Minjae Ju, Sumin Lee and Jaewoo Kang. "ArkDTA: Attention Regularization guided by non-Covalent Interactions for Explainable Drug-Target Binding Affinity Prediction", ISMB/ECCB 2023, July 23--27, 2023, Lyon, France, (Full Paper Accepted, Oral Presentation, Bioinformatics, vol. 39, https://doi.org/10.1093/bioinformatics/btad207)

  • Mogan Gim*, Donghee Choi*, Kana Maruyama, Jihun Choi, Hajung Kim, Donghyeon Park and Jaewoo Kang. "RecipeMind: Guiding Ingredient Choices from Food Pairing to Recipe Completion using Cascaded Set Transformer", CIKM '22: Proceedings of the 31st ACM International Conference on Information and Knowledge Management, October 17--21, 2022, Atlanta, GA, USA, https://doi.org/10.1145/3511808.3557092 (Full Paper Accepted, Oral Presentation)

  • Mogan Gim*, Donghyeon Park*, Michael Spranger, Kana Maruyama and Jaewoo Kang. "RecipeBowl: A Cooking Recommender for Ingredients and Recipes using Set Transformer", IEEE Access, vol. 9, October 2021, https://doi.org/10.1109/ACCESS.2021.3120265'

Research Area

Projects

  • 2025년 산학연 CollaboR&D [2025, ongoing]

    • Funded by 중소기업기술정보진흥원

    • 자율학습 및 RAG 기반 기업 맞춤형 화학물질 특화 sLLM 시스템 개발

      • Phase 1 Project

    • 2025.05.01~2025.12.31 (15,000,000 won)

  • Sony Research [2020 ~ 2024]

    • Collaborative Work with Sony AI Gastronomy Project

    • CookingSense: A Culinary Knowledgebase with Multidisciplinary Assertions (paper)

    • KitchenScale: Learning to predict ingredient quantities from recipe contexts (paper, github)

    • RecipeMind: Guiding Ingredient Choices from Food Pairing to Recipe Completion using Cascaded Set Transformer (paper, github)

    • RecipeBowl: A Cooking Recommend for Ingredients and Recipes Using Set Transformer (paper, github)

  • SK pucore [2018~ 2021]

    •  Collaborative Work with SK pucore's SMARTUS Development Project (as Lead Resarcher/Developer)

    • Improved the SMARTUS System with Enhanced Prediction Model and Recomendation Algorithm

    • Developed and Deployed a Server-based System fo Urethane Recipe Formulation (SMARTUS)

    • Developed a Deep Learning Mdoel for Property Prediction of Polyurethane Products

    • Developed a Recommendation Algorithm for Polyurethane Recipe Formulations

  • SK Nexilis [2020~ 2021]

    •  Collaborative Work with SK Nexilis' Development Project (as Developer)

Miscellaneous

  • Patents

    • METHOD AND SERVER FOR FOOD INGREDIENT PAIRING PREDICTION USING SIAMESE NEURAL NETWORk (10-2019-0158385), https://doi.org/10.8080/1020230049561

    • METHOD AND APPARATUS FOR BINDING AFFINITY PREDICTION BASED  ON NON-COVALENT INTERACTION (10-2023-0049561), https://doi.org/10.8080/1020230049561

  • Competitions

    • [2018] NCI-CPTAC Multi-omics Enabled Sample Mislabeling Correction Challenge, PrecisionFDA

    • [2020] Preterm Birth Prediction: Transcriptomics, DREAM Challenge, 8th Place

    • [2022] Preterm Vaginal Microbiome Challenge, DREAM Challenge, 5th Place

  • Paper Review

    • [2024] BMC Bioinformatics, (IF: 3.169)

    • [2024] Information Processing and Management, (IF: 6.222)

  • Talk

    • [2025-1] Invited Talk at Korea University, College of Health Science 

    • [2025-1] Invited Talk at APCMS 2025 (link)

  • Lecture

    • [2024-2] Digital Engineering (Undergraduate Course)

    • [2024-2] BME Electronic Circuits (Undergraduate Course)

    • [2025-1] Academic English Writing (Graduate Course)

    • [2025-1] Cheminformatics (Undergraduate Course)

    • [2025-1] Biomedical Artificial Intelligence (Undergraduate Course)

    • [2025-2] Biomedical NLP (Undergraduate Course)

    • [2025-2]  Drug Discovery (Undergraduate Course)

Former Name: Keonwoo KimEmail: gimmogan@hufs.ac.kr
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