About Me
Hi! I am a 3rd year CSE Ph.D. student at the University of Washington, advised by Su-In Lee at Artificial Intelligence for Biological and Medical Sciences (AIMS). Before UW, I was a senior clinical data scientist at Philips Research North America (PRNA), specializing in developing clinical decision support systems with machine learning. I completed an MD degree at Kaohsiung Medical University and a Master of Biomedical Informatics at Harvard Medical School and did 1-year postdoctoral research at Laboratory for Computational Physiology at MIT. During my time in medical school, I cofounded TheTinyNote, a website of physician-authored clinical decision support resources, allowing medical professionals to follow the more than 1500+ latest guidelines of diseases and clinical inquiry.
You can reach out to me at mingyulu[at]cs[dot]washington[dot]edu
Research Interest
My research mainly focuses on Transparent Machine Learning and Machine Learning for Healthcare (ML4H). More specifically, I study sequential decision-making (reinforcement learning) and counterfactual prediction (g-methods).
Publications
Conference
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MingYu Lu, Zach Shah, Finale Doshi Velez, Li-Wei Lehman. Is Deep Reinforcement Learning Ready for Practical Applications in Healthcare? A Sensitivity Analysis of Duel-DDQN for Sepsis Treatment. AMIA 2020 Distinguished Paper. [paper]
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Ian Connick Covert, Wei Qiu, MingYu Lu, Na Yoon Kim, Nathan J White, Su-In Lee. Learning to maximize mutual information for dynamic feature selection ICLR 2023 [paper]
Workshop
- Chris Lin, Mingyu Lu, Su-In Lee. Explaining Diffusion Models via Removal-Based Attribution. Navigating and Addressing Data Problems for Foundation Models at ICLR 2024 Workshop
- MingYu Lu, Yifang Chen, Su-In Lee. A Deep Bayesian Bandits Approach for Anticancer Drug Screening: Exploration via Functional Prior. Adaptive Experimental Design and Active Learning in the Real World Workshop at ICML 2022 [paper]
- Rui Li, Stephanie Hu, Yuria Utsumi, MingYu Lu, Prithwish Chakraborty, Daby Sow, Piyush Madan, Jun Li, Mohamed Ghalwash, Zachary Shahn, Li-wei H Lehman. G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimes. Machine Learning for Health (ML4H) Workshop at NeurIPS 2021.[paper]
- Niklas Rindtorff, MingYu Lu, Nisarg Patel, Huahua Zheng, and Alexander D’Amour. A Biologically Plausible Benchmark for Contextual Bandit Algorithms in Precision Oncology Using In vitro Data. Machine Learning for Health (ML4H) Workshop at NeurIPS 2019. [paper]
Abstract & Symposium:
- Chenyu Lu, Jingyi Chen, MingYu Lu, Leo Anthony Celi. Predicting Hemodilution with Machine Learning. Beth Israel Deaconess Medical Center Artificial Intelligence/Machine Learning Symposium 2020
- Rui Li, Zach Shahn, Jun Li, Mingyu Lu, Prithwish Chakraborty, Daby Sow, Mohamed Ghalwash, Li-wei H. Lehman. Learning Optimal DTRS From Temporal Icu Monitoring Data. MIT-IBM Watson AI Lab Virtual Poster Session 2020
Preprint
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 |
| 2014 | Chief Information Officer of Kaohsiung Medical University Class of 2017 |
| 2013 | President of Guitar Club Kaohsiung Medical University |