معرفی
Fang Liu serves as Notre Dame Collegiate Professor and Associate Chair in the Department of Applied and Computational Mathematics and Statistics (ACMS) at the University of Notre Dame's College of Science. She holds a Ph.D. from the University of Michigan (2003), M.S. from Iowa State University (1999), and B.S. from Peking University (1997).
- Ph.D., University of Michigan, 2003
- M.S., Iowa State University, 1999
- B.S., Peking University, 1997
Her research spans five interconnected domains: 1) Development of modern data privacy protection approaches including differential privacy; 2) Statistical and machine learning methodologies with complex model regularization; 3) Bayesian frameworks for medical, biological and social science data analysis; 4) Advanced missing data analysis techniques; and 5) Biostatistical and epidemiological applications. She pioneered the integration of differential privacy concepts from computer science into statistical methodology, identifying numerous research opportunities at this interdisciplinary intersection.
Dr. Liu leads the Trustworthy and Ethical Statistical Learning Lab (TESLLa), which focuses on differential privacy, trustworthy AI, synthetic data generation, and probabilistic machine learning. Her lab has produced significant output in privacy-preserving statistical inference and applications to pandemic response, vector-borne disease control, and social media emotional analysis.
Key honors include:
- Elected Fellow of the American Statistical Association
- Elected Member of the International Statistical Institute (2024)
As an advisor, she has mentored over 40 graduate students and researchers since joining Notre Dame in 2011, with alumni securing positions at institutions including Sandia National Laboratories, Los Alamos National Lab, JP Morgan Chase, TikTok, and academic faculty roles. Her research has received substantial funding from NSF, NIH, private foundations, and Notre Dame internal grants, including critical work on campus COVID-19 surveillance testing strategy and privacy-preserving pandemic data collection.
