Short Bio

I am a Ph.D. student in Computer Science & Engineering at the University of Washington, where I’m fortunate to be working with Prof. Su-In Lee. My research interests lie at the intersection of machine learning and biomedicine. During my PhD, I have interned at Microsoft Research – Health Futures with Dr. Hoifung Poon’s group and at Google Health with the Personal Health Agent team. Selected recognitions include the Forbes 30 Under 30 in Healthcare & Science category and the Asan Foundation Biomedical Science Scholarship.

Before joining UW, I graduated summa cum laude with a B.S. in Electrical & Computer Engineering from Seoul National University. During my undergraduate studies, I worked on various computational biology topics under the supervision of Prof. Buhm Han.

Research Interests: Machine Learning, Transparent & Trustworthy AI, AI for Healthcare, Computational Biology, Vision Language Models

Research

Transparent & Safe Medical AI: I develop frameworks to build transparent and safe medical AI systems, an essential component in gaining trust from clinicians and patients as well as ensuring compliance with regulatory standards.
- How foundation models can bridge transparency gaps in medical AI systems [Nature Medicine 2024]
- Guidelines for building transparent medical AI systems [Nature Reviews Bioengineering 2025]
- How interpretability helps control medical AI models [Manuscript under preparation]
Explainable & Accountable AI: I develop ML methods to understand and explain the complex behavior of AI models, with a focus on large-scale models.
- Real-time Shapley value estimation for vision transformer models [ICLR 2023 Spotlight]
- Unified approach to accelerate feature and data attribution [NeurIPS 2024]
- Tracing diffusion model output to training data [ICLR 2025]
AI for Biological Discovery: I build AI tools to accelerate biological discovery and identify novel therapeutic targets.
- Multimodal AI for population-scale biological discovery in spatial biology [Cell 2025]
- Discovering novel cell-type marker genes from scRNA-seq data without prior clustering [Nucleic Acids Research 2022]
- Image-text foundation model for learning perturbation effects in cell painting [NeurIPS 2025]

Publications / Preprints

Dissecting and directing pathology foundation models
Chanwoo Kim, Jakub Kaczmarzyk, Deepika Savant, Zhen Zhao, Peter K. Koo^†, and Su-In Lee^† bioRxiv, 2026 [paper]

Multimodal AI generates virtual population for tumor microenvironment modeling
Jeya Maria Jose Valanarasu *, Hanwen Xu *, Naoto Usuyama *, Chanwoo Kim *, […], Carlo Bifulco^†, Sheng Wang^†, and Hoifung Poon^†
Cell, 2025 [paper] [GeekWire] [CNBC] [highlighted by Microsoft CEO]

CellCLIP – Learning Perturbation Effects in Cell Painting via Text-Guided Contrastive Learning
Mingyu Lu *, Ethan Weinberger *, Chanwoo Kim, and Su-In Lee^†
Neural Information Processing Systems (NeurIPS), 2025 [paper]

Transparency of medical artificial intelligence systems
Chanwoo Kim *, Soham U. Gadgil *, and Su-In Lee^†
Nature Reviews Bioengineering, 2025 [paper] [ScienMag] [UW News]

Efficient Shapley Values for Attributing Global Properties of Diffusion Models to Data Group
Chris Lin *, Mingyu Lu *, Chanwoo Kim, and Su-In Lee^†
International Conference on Learning Representations (ICLR), 2025 [paper]

Stochastic Amortization: A Unified Approach to Accelerate Feature and Data Attribution
Ian Covert *, Chanwoo Kim *, Su-In Lee^†, James Zou^†, and Tatsunori Hashimoto^†
Neural Information Processing Systems (NeurIPS), 2024 [paper] [code]

Transparent medical image AI via an image-text foundation model grounded in medical literature
Chanwoo Kim, Soham U. Gadgil, Alex J. DeGrave, Jesutofunmi A. Omiye, Zhuo Ran Cai, Roxana Daneshjou^†, and Su-In Lee^†
Nature Medicine, 2024 [paper] [code] [press] [Allen School News] [Nature behind the paper]

Learning to Estimate Shapley Values with Vision Transformers
Ian Covert *, Chanwoo Kim *, and Su-In Lee^†
International Conference on Learning Representations (ICLR), 2023 [paper] [code]
Spotlight Presentation (top 5% of submitted papers)

Contrastive Corpus Attribution for Explaining Representations
Chris Lin *, Hugh Chen *, Chanwoo Kim, and Su-In Lee^†
International Conference on Learning Representations (ICLR), 2023 [paper] [code]

MarcoPolo: a method to discover differentially expressed genes in single-cell RNA-seq data without depending on prior clustering
Chanwoo Kim *, Hanbin Lee *, Juhee Jeong, Keehoon Jung^†, and Buhm Han^†
Nucleic Acids Research, 2022 [paper] [code] [demo] [press(kor)(eng)]

Phenome-wide association study of the major histocompatibility complex region in the Korean population identifies novel association signals
Chanwoo Kim *, Young Jin Kim *, Wanson Choi, Hye-Mi Jang, Mi Yeong Hwang, Sunwoo Jung, Hyunjoon Lim, Sang Bin Hong, Kyungheon Yoon, Bong-Jo Kim, Hyun-Young Park, and Buhm Han^†
Human Molecular Genetics, 2022 [paper] [data]

*: equal contribution, † : corresponding author [J]: journal, [C]: conference, [P]: preprint

Education

University of Washington
- Ph.D. student in Computer Science and Engineering
- Sep. 2021 – Present
Seoul National University
- B.S. in Electrical and Computer Engineering, Summa Cum Laude
- Mar. 2015 – Feb. 2021 (2-year leave for mandatory military service)
- Total GPA: 4.11/4.3 (Major: 4.14/4.3, Upper: 4.19/4.3)

Research Experience

Graduate Research Assistant, University of Washington (Sep. 2021 – Present)
- AI for Biomedical Sciences (AIMS) Lab
- Advised by Prof. Su-In Lee
Student Researcher, Google Health (Jan. 2026 – Jun. 2026)
- Personal Health Agent Research Team
- Host: Dr. Ali Heydari and Dr. Xin Liu
Research Intern, Microsoft Research (Jun. 2024 – Sep. 2024)
- Health Futures, Real-world evidence
- Worked with Dr. Hoifung Poon’s group
Undergraduate Research Assistant, Seoul National University (Mar. 2019 – Aug. 2021)
- Genomics & Bioinformatics Lab
- Advised by Prof. Buhm Han

Honors and Awards

Forbes 30 Under 30 Asia, Forbes (May 2026)
- Recognized for research on transparent, trustworthy AI for healthcare
Asan Foundation Biomedical Science Scholarship, Asan Foundation (Mar. 2023 – Jun. 2026)
- Awarded to graduate students for excellence in biomedical science research
UW Azure Cloud, UW eScience Institute (Nov. 2022)
- Awarded $20,000 in Azure cloud credits for research proposal
Eminence Scholarship, SNU (Mar. 2016 – Feb. 2020)
- Full tuition waiver based on academic merit
Commendation of Seoul 5th Riot Police Commander, Korean National Police Agency (Dec. 2018)
- Recognized for contribution to public safety during military service
SK Networks Choi Shin-Won Scholarship, SK Networks (Mar. 2012 – Feb. 2015)
- Full tuition waiver based on academic merit

Teaching

Teaching Assistant, University of Washington
- CSE 527 Computational Biology (Fall 2023)
- CSE 599 Explainable AI (Spring 2023 and Spring 2024)
Guest Lecturer, University of Washington
- “Sparse autoencoder” in CSE 599 Explainable AI class at UW CSE (May 14, 2026)
- “Amortized optimization” in CSE 599 Explainable AI class at UW CSE (Apr 28, 2026)
- “Transparency for medical AI” in CSE 427 Computational Biology class at UW CSE (Nov 20, 2025)
- “LLM explainability” in CSE 599 Explainable AI class at UW CSE (May 23, 2024; May 29, 2025, May 28, 2026)
- “XAI in healthcare” in CSE 599 Explainable AI class at UW CSE (May 30, 2024; Jun 5, 2025, Jun 4, 2026)
- “LLM explainability” in CSE 527 Computational Biology class at UW CSE (Nov 5, 2024)
Undergraduate Teaching Assistant, Seoul National University
- Digital Systems Design and Experiments (Fall 2019 and Fall 2020)
- Programming Methodology (Spring 2017)
- Basic Physics (Spring 2016, Fall 2016, and Spring 2017)
Peer Tutoring Program, Seoul National University
- Taught a junior from the same department about major subjects. (Spring 2020)

Professional Services

Reviewer
- Journal: Nature, Nature Medicine, npj Digital Medicine, npj Precision Oncology, eLife
- Main conference: NeurIPS 2023/2026, ICML 2024/2025, ICLR 2025, RECOMB 2024.
- Workshop: AAAI 2024 Symposium on Clinical Foundation models, NeurIPS 2024 Workshop InterpretableAI, ICML 2026 Workshop on Structured Data for Health, Machine Learning in Computational Biology 2026 .
Panelist
- Guided discussions on “Explainability, Interpretability, and Transparency” at the CHIL (Jun 27, 2025)
Allen school GEAR Program, UW CSE (2023 – Present)
- Mentored 8 potential PhD program applicants in total in Pre-Application Mentorship Service program, with a focus on supporting underrepresented groups
- Served as panelist in PhD application information session (Oct 17, 2024; Nov 20, 2025)
Graduate Applications Reader, UW CSE (2021 – 2023)
- Reviewed PhD applications

Other Experiences

Auxiliary Police, Korean National Police Agency (Jan. 2018 – Aug. 2019)
- Worked as security guard at National Police Agency
- Worked as secretary to Commissioner-General
Deepest, SNU (Aug. 2017 – Dec. 2017)
- Engaged in deep learning research society organized by SNU students
Korean Tutoring Program, SNU (Aug. 2017)
- Taught Korean language to foreign exchange students
Golbaengi, SNU (Mar. 2016 – Jul. 2016)
- Took care of children with developmental disorders in local community
Exchange Program, Peking University (Jun. 2016 – July. 2016)
- Participated in Globex Julmester, summer exchange program, taking engineering courses and engaging in cross-cultural activities.