Background
Guifang Fu's long-term career goal is to strengthen the mathematical and statistical foundations of machine learning and artificial intelligence through the development of novel methodology and theory motivated by real-world scientific problems. She is particularly interested in applications involving morphology and shape analysis, the microbiome, genome-wide association studies (GWAS), and other biomedical fields. These application domains motivate the development of innovative analytical strategies that leverage rigorous statistical principles in functional and longitudinal data analysis, together with state-of-the-art machine learning and deep learning techniques. She also develops theoretical results for high-dimensional statistical inference.
Fu is committed to interdisciplinary collaboration and the training of the next generation of statisticians. She works closely with researchers in mathematics, computer science, biomedical research, anthropology, and other disciplines. Her research was supported by a National Science Foundation award and multiple internal research grants.
Recent Publications and Preprints
- Niranda P, McKenney P, and Fu G*. Enhanced Edge Selection Approaches for ODE Graph Network Construction.
- Zhao G†, Li X†, Chavoshnejad P, Razavi J, Solhtalab A, Yin L, and Fu G*. Toward Highfidelity 3D Point-Cloud Learning for Brain Folding Morphology Prediction Using Trans-Unet.
- Wang Y†, Thakar S†, Schick A, and Fu G*. Theoretical Properties of Multivariate Random Forest in Feature Selection and its Application to Facial Morphology-Gene Detection.
- Zhao S and Fu G* (2022). Distribution-free and Model-free Multivariate Feature Screening via Multivariate Rank Distance Correlation. 192, 105081.
- Dai X, Fu G*, Reese R, Zhao S, and Shang Z (2022). An Approach of Bayesian Variable Selection for Ultrahigh Dimensional Multivariate Regression.
Education
- PhD in Statistics, Pennsylvania State University
- Master's in Mathematics, the University of Florida
Research Interests
- Statistical Machine Learning
- Statistical Shape Analysis
- Functional/Longitudinal Analysis
- High-dimensional Statistical Inference
- Biostatistics
- Genome-Wide Association Studies (GWAS)
- Microbiome, Neuroscience, and other Biomedical Applications
Awards
- NSF Award (DMS-1413366): Statistical Models for Mapping Genetic and Environmental Effects Regulating Shape Variation