1). Predictive modeling of clinical risks
2). Disease subtyping
3). Computational drug discovery and design
4). Knowledge graph
5). Clinical trial emulation with real world data
The Wang Lab is conducting research on developing machine learning models to perform integrative analysis of massive biomedical data and derive actionable insights.
We are developing modern machine-learning approaches for analyzing massive biomedical data to better understand disease mechanisms and deriving actionable insights. There are two main streams in our research. One is algorithm development. We are looking into various aspects of developing effective biomedical machine-learning models, such as multimodal learning, federated learning, model interpretability and explainability, algorithmic fairness, causal inference, etc. The other is application, where we apply the algorithms developed to help with real-world biomedical problems, such as disease risk prediction, disease subtyping, computational drug repurposing, clinical trial emulation, etc.
Biography
Dr. Fei Wang is a tenured professor of population health sciences in the Division of Health Informatics. His research interests are machine learning and artificial intelligence in biomedicine. He is the founding director of the Institute of AI for Digital Health. Dr. Wang has published in major AI venues including NeurIPS, ICML, AAAI and KDD, as well as major medical venues including Nature Medicine, Annals of Internal Medicine, and JAMA Internal Medicine. Dr. Wang is an elected fellow of American Medical Informatics Association, American College of Medical Informatics, and International Academy of Health Sciences and Informatics, and a distinguished member of Association for Computing Machinery. Dr. Wang's research has been extensively funded by federal agencies including NIH, NSF and ONR, private foundations including MJFF and AHA, as well as industries such as Amazon, Google, Boehringer Ingelheim, Regeneron and Sanofi.
Distinctions:
Fellow, American College of Medical Informatics (ACMI)
Fellow, International Academy of Health Sciences and Informatics (IAHSI)
Distinguished Member, Association for Computing Machinery (ACM)
Fellow, American Medical Informatics Association (AMIA)
Research Leadership Award. IEEE International Conference on Health Informatics (ICHI)
NSF CAREER Award
Selected Publications:
Hao Zhang, Chengxi Zang, Zhenxing Xu, Yongkang Zhang, Jie Xu, Jiang Bian, Dmitry Morozyuk, Dhruv Khullar, Yiye Zhang, Anna S Nordvig, Edward J Schenck, Elizabeth A Shenkman, Russell L Rothman, Jason P Block, Kristin Lyman, Mark G Weiner, Thomas W Carton, Fei Wang*, Rainu Kaushal. (* Corresponding Author). Data-driven identification of post-acute SARS-CoV-2 infection subphenotypes. Nature Medicine. 29, pages 226–235 (2023).
Chengxi Zang, Yongkang Zhang, Jie Xu, Jiang Bian, Dmitry Morozyuk, Edward J Schenck, Dhruv Khullar, Anna S Nordvig, Elizabeth A Shenkman, Russell L Rothman, Jason P Block, Kristin Lyman, Mark G Weiner, Thomas W Carton, Fei Wang*, Rainu Kaushal. (* Corresponding Author) Data-driven analysis to understand long COVID using electronic health records from the RECOVER initiative. Nature Communications. 14, Article number: 1948 (2023).
Chengxi Zang, Hao Zhang, Jie Xu, Hansi Zhang, Sajjad Fouladvand, Shreyas Havaldar, Feixiong Cheng, Kun Chen, Yong Chen, Benjamin S. Glicksberg, Jin Chen, Jiang Bian, Fei Wang*. (* Corresponding Author) High-throughput target trial emulation for Alzheimer’s disease drug repurposing with real-world data. Nature Communications. 14, Article number: 8180 (2023).
Fei Wang*, Rainu Kaushal, Dhruv Khullar. (* Corresponding Author) Should Health Care Demand Interpretable Artificial Intelligence or Accept “Black Box” Medicine? Annals of Internal Medicine. Volume 172, Issue 1. Page: 59-60. 2020.
Fei Wang*,, Lawrence Peter Casalino, and Dhruv Khullar. (* Corresponding Author) "Deep learning in medicine—promise, progress, and challenges." JAMA Internal Medicine 179, no. 3 (2019): 293-294.
