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headshot of Guifang Fu

Guifang Fu

Associate Professor

Department of Mathematics and Statistics
WH 133

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 driven by real-world datasets and scientific applications. She is particularly interested in applications involving morphology and shape analysis, the microbiome, genome-wide association studies (GWAS), and other biomedical areas. These application domains motivate the development of innovative data analytical strategies that leverage rigorous statistical methodology in functional and longitudinal data analysis, variable selection, and statistical inference, as well as state-of-the-art machine learning and deep learning techniques. Her group also develops theoretical guarantees for high-dimensional statistical learning methods.

Fu is committed to interdisciplinary collaboration and the training of the next generation of statisticians. She works closely with researchers in mathematics, computer science, biomedicine, anthropology, and other disciplines. Her research has been supported by the National Science Foundation 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 (RECON).
  • Zhao G†, Li X†, Chavoshnejad P, Razavi J, Solhtalab A, Yin L, and Fu G*. Toward High-fidelity 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). . Journal of Multivariate Analysis. 192, 105081. 
  • Dai X, Fu G*, Reese R, Zhao S, and Shang Z (2022).. Stat. 1(1), e476.

Education

  • PhD in Statistics, Pennsylvania State University
  • MS in Mathematics, the University of Florida


Research Interests

  • Statistical Machine Learning
  • Data Science 
  • 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

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