معرفی
Xiaoming Huo is the A. Russell Chandler III Professor at the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech, where he also serves as Associate Director for Research at the Institute for Data Engineering and Science (IDEaS). He holds executive leadership positions including Director of the NSF-funded Transdisciplinary Research Institute for Advancing Data Science (TRIAD) and oversees Georgia Tech's Master of Science in Analytics program in Shenzhen.
Education includes:
- Ph.D. in Statistics from Stanford University (1999)
- M.S. in Electrical Engineering from Stanford University (1997)
- B.S. in Mathematics from University of Science and Technology of China (1993)
Dr. Huo's research integrates statistical theory with computational methods, focusing on:
- Foundational machine learning: Theoretical analysis of deep neural networks, adversarial training frameworks
- High-dimensional statistics: Sparse modeling, regularization techniques, and minimax optimization
- Data science applications: Anomaly detection, generative modeling, and domain adaptation methods
Recent publications (2023-2025) demonstrate strong focus on: adversarial learning frameworks, neural network theory, anomaly detection systems, and high-dimensional statistical methods. Common themes include theoretical guarantees for deep learning architectures, optimization in statistical estimation, and robust model formulations.
Significant scientific recognition includes:
- Golden Prize, International Mathematical Olympiad (1989)
- IEEE Senior Member (2004)
- Sigma Xi Young Faculty Award (2005)
- Emerging Research Fronts in Mathematics (2006)
- Multiple competitive fellowships during academic training
Leadership in major NSF initiatives includes directing TRIAD and contributing to the NSF AI Institute: ACTION. Manages interdisciplinary teams across data engineering, statistical theory, and machine learning applications.
