Dr. Feifan Liu is an accomplished scientist specializing in the application of artificial intelligence to complex healthcare challenges. He currently serves as Associate Professor in the Department of Population and Quantitative Health Sciences at University of Massachusetts Chan Medical School, with a focus in the Division of Health Informatics and Implementation Science. Following doctoral training in pattern recognition and artificial intelligence at the Chinese Academy of Sciences, he completed postdoctoral research at the University of Wisconsin-Milwaukee before gaining industry experience at Nuance Communications. Dr. Liu established his independent academic career at UMass Medical School in 2017, founding and directing the innovative AI for Health (iAI4Health) laboratory, which has become a premier research center at the intersection of machine learning and healthcare analytics.
Dr. Liu's groundbreaking research in natural language processing and deep learning has fundamentally transformed approaches to heterogeneous healthcare data analysis for clinical decision support. His work on extracting gene mutation-disease relations through the DeepGeneMD model, presented at EMNLP 2019, established new benchmarks in biomedical text mining and earned first place in the international Gene Mutation/Disease Relation Extraction challenge. With over 70 peer-reviewed publications including more than 30 as first-author, his methodological contributions have advanced capabilities in suicide risk prediction, HIV prevention, cancer informatics, and cardiometabolic disease management. His innovative approaches to transforming unstructured clinical notes into actionable insights for identifying breast cancer recurrence and medication adverse events have demonstrated significant potential to improve patient outcomes through precision health interventions.
Beyond his technical contributions, Dr. Liu has emerged as a leader in addressing health equity through artificial intelligence, serving as a scientific advisor for the NIH All of Us research program and the NIH AIM-AHEAD leadership fellowship since 2023. His recent work focuses on assessing and mitigating algorithmic biases in healthcare applications, positioning him at the forefront of responsible AI implementation in medicine. Through his active participation in NIH and NSF study sections as well as his recognition as an NIH AIM-AHEAD leadership fellow in 2022, Dr. Liu continues to shape the direction of computational health research funding and priorities. His ongoing efforts to develop fair and transparent AI systems that advance health equity while improving predictive capabilities demonstrate his commitment to creating technologies that serve diverse patient populations with equal effectiveness.
