Hao Chen

Hao Chen, Ph.D. is an Associate Professor in the Department of Statistics at the University of California, Davis. His research focuses on statistical methodology for high-dimensional and non-Euclidean data, including anomaly detection, graph-based methods, and change-point analysis. He also explores AI security, multimodal models, and geospatial applications. His work bridges statistical theory and practical machine learning challenges.

Education:

• Ph.D., Graduate Group in Biostatistics, Stanford University

Research Interests: Dr. Chen’s expertise spans statistical methods for streaming data, categorical data analysis, and allele-specific copy number variation. He has pioneered work in detecting signals in complex datasets and developing robust AI systems. His recent focus includes mitigating modality interference in LLMs, enhancing model safety via guardrails, and advancing geospatial AI through projects like GeoLM.

Publications: His recent work addresses cutting-edge topics such as multimodal model vulnerabilities, unlearning algorithms, and clinical radiology applications. Key themes include improving model robustness, ethical AI design, and interdisciplinary data integration.

Labs/Teams: Engages in collaborative projects at UC Davis, though specific lab names are not listed in the provided information.