About me
Hi! I am a 5th-year computer science & engineering (CSE) Ph.D. student at the University of Washington, advised by Su-In Lee in the Artificial Intelligence for Biological and Medical Sciences (AIMS) group. My research focuses on explainable and interpretable machine learning, particularly on attribution methods that decompose model behaviors into the contributions of different components, including individual features, samples, or data sources. I am also passionate about applying these techniques to accelerate real-world impact in domains such as healthcare and drug discovery.
Previously, I worked with Li-wei Lehman, Zach Shahn, and Finale Doshi-Velez at Harvard and MIT. I have spent summers interning at research labs in academia and industry: Laboratory for Computational Physiology at MIT, and Bosch Research. I earned my MD from Kaohsiung Medical University and completed a master’s degree in Biomedical Informatics at Harvard Medical School’s Department of Biomedical Informatics.
I’ve been fortunate to work with many amazing people, and I’m always excited about new opportunities to collaborate. You can reach out to me at mingyulu[at]cs[dot]washington[dot]edu
Recent update
- (September 2025) CellCLIP – Learning Perturbation Effects in Cell Painting via Text-Guided Contrastive Learning has been accepted to NeurIPS 2025!
- (June 2025) I will be working with Suraj Srinivas and Jorge Piazentin Ono at Bosch Research during the summer of 2025!
Conference publications
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CellCLIP – Learning Perturbation Effects in Cell Painting via Text-Guided Contrastive Learning
Mingyu Lu, Ethan Weinberger, Chanwoo Kim, Su-In Lee
Neural Information Processing Systems (NeurIPS), 2025
[paper] [code] [project page] -
BehaviorSFT: Behavioral Token Conditioning for Clinical Agents Across the Proactivity Spectrum
Yubin Kim, Zhiyuan Hu, Hyewon Jeong, Eugene W Park, Shuyue Stella Li, Chanwoo Park, Shiyun Xiong, MingYu Lu, Hyeonhoon Lee, Xin Liu, Daniel McDuff, Cynthia Breazeal, Samir Tulebaev, Hae Won Park
Findings of Empirical Methods in Natural Language Processing (EMNLP), 2025
[paper] [code] [project page] -
An Efficient Framework for Crediting Data Contributors of Diffusion Models
Chris Lin *, Mingyu Lu * , Su-In Lee
International Conference on Learning Representations (ICLR), 2025
[paper] [code] [project page] -
Learning to Maximize Mutual Information for Dynamic Feature Selection
Ian Connick Covert, Wei Qiu, MingYu Lu, Na Yoon Kim, Nathan J White, Su-In Lee
International Conference on Machine Learning (ICML), 2023
[paper] [code] -
G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimes.
Rui Li, Stephanie Hu, Yuria Utsumi, MingYu Lu, Prithwish Chakraborty, Daby Sow, Piyush Madan, Jun Li, Mohamed Ghalwash, Zachary Shahn, Li-wei H Lehman
Machine Learning for Health (ML4H), 2021
[paper] -
Is Deep Reinforcement Learning Ready for Practical Applications in Healthcare? A Sensitivity Analysis of Duel-DDQN for Sepsis Treatment.
MingYu Lu, Zach Shah, Finale Doshi Velez, Li-Wei Lehman.
American Medical Informatics Association (AMIA), 2020
Distinguished Paper
[paper]
Workshop and prepints
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MedBLINK:Probing Visual Perception and Trustworthiness in Multimodal Language Models for Medicine Mahtab Bigverdi, Wisdom Oluchi Ikezogwo, Kevin Minghan Zhang, Hyewon Jeong, MingYu Lu, Sungjae Cho, Linda Shapiro, Ranjay Krishna
Computer Vision for Automated Medical Diagnosis (CVAMD) at ICCV, 2025
Oral presentation
[paper] [project page] [hf dataset] -
Tiered Agentic Oversight: A Hierarchical Multi-Agent System for AI Safety in Healthcare
Yubin Kim, Hyewon Jeong, Chanwoo Park, MingYu Lu, Eugene W Park, Haipeng Zhang, Xin Liu, Hyeonhoon Lee, Daniel McDuff, Cynthia Breazeal, Samir Tulebaev, Hae Won Park
Multi-Agent Systems (MAS) in the Era of Foundation Models at ICML, 2025
[paper] -
Medical Hallucination in Foundation Models and Their Impact on Healthcare
Yubin Kim, Hyewon Jeong, Shan Chen, Shuyue Stella Li, Mingyu Lu, Kumail Alhamoud, Jimin Mun, Cristina Grau, Minseok Jung, Rodrigo Gameiro, Lizhou Fan, Eugene Park, Tristan Lin, Joonsik Yoon, Wonjin Yoon, Maarten Sap, Yulia Tsvetkov, Paul Liang, Xuhai Xu, Xin Liu, Daniel McDuff, Hyeonhoon Lee, Hae Won Park, Samir Tulebaev, Cynthia Breazea
medRxiv, 2025
[paper] [code] [project page] -
LIFT - XAI: Leveraging Important Features in Treatment Effects to Inform Clinical Decision-Making via Explainable AI, Chanwoo Kim, Ian Covert, Nathan J White, Su-In Lee
medRxiv, 2024
[paper] -
A Deep Bayesian Bandits Approach for Anticancer Drug Screening: Exploration via Functional Prior.
MingYu Lu, Yifang Chen, Su-In Lee
Adaptive Experimental Design and Active Learning in the Real World Workshop at ICML, 2022
[paper]
Leadership/Awards
| Year | Leadership/Awards |
|---|---|
| 2020 | Organizer of NewInML at NeurIPS 2020 |
| 2019 | LEAP Fellowship of the Ministry of Science and Technology of Taiwan. |
| 2017 | CoFounder of TinyNote |
Academic service
- Conference reviewing: NeurIPS (2024, 2025), ICLR (2025), AMIA (2020), CHIL (2020), ML4H@NeurIPS (2019-2020), DPFM@ICLR (2025), LMRL@ICLR (2025)