Feng Xie

Feng Xie is an Assistant Professor at the University of Minnesota, affiliated with the Division of Computational Health Sciences in the Department of Surgery and the Graduate Faculty of Data Science in the College of Science and Engineering. With a joint PhD from Duke University and National University of Singapore, his academic journey includes postdoctoral work at Stanford University School of Medicine under Dr. Nima Aghaeepour.

• Education
  • PhD (Integrated Biology and Medicine), Duke-NUS Medical School (2022)
  • BS (Biomedical Engineering), Tsinghua University (2017)

His research combines medical informatics, machine learning, and biostatistics to develop trustworthy AI/ML solutions for healthcare challenges. Key focus areas include electronic health records (EHR) modeling, clinical decision support systems, and multimodal data integration across domains like emergency medicine, critical care, and maternal-child health.

Recent research trends emphasize interpretable AI through publications like AutoScore frameworks and Grade for Interpretable Field Triage models. His work on NeonatalBERT and PregMedNet demonstrates expertise in domain-specific language models and maternal medication impact analysis.

The lab provides exclusive access to millions of patient records and prioritizes high-impact publications in journals like JAMA Network and The Lancet Digital Health. Infrastructure includes established data pipelines and robust computing resources for scalable health data analysis.