Ping Ma

Ping Ma is a Professor of Statistics with a courtesy appointment in Computer Science at the University of Georgia. His research focuses on developing innovative statistical and machine learning methodologies for complex high-dimensional data, with applications spanning bioinformatics, computational biology, social network analysis, and anomaly detection in power systems.

Research interests include:

• Statistical Methodology: Nonparametric modeling, optimal transport theory, subsampling techniques, and functional regression for large-scale data
• Computational Biology: Spatial transcriptomics analysis, single-cell data integration, virology classification, and gene regulatory networks
• Machine Learning Innovations: Knowledge distillation for LLMs, tensor analysis, quantum-inspired algorithms, and ensemble learning for model robustness

His recent publications demonstrate a strong trend toward interdisciplinary applications, particularly in developing AI/statistical tools for biomedical research (47% of recent papers), advancing foundational machine learning techniques (33%), and solving engineering challenges like power grid security (20%). Methodologically, 67% focus on novel algorithm development while 33% refine existing techniques for scalability.