A. Russell Chandler III Professor and
Associate Director for Research in the Institute for Data Engineering and Science (IDEaS)
Education
- Ph.D. Statistics (1999), Stanford University
- M.S. Electrical Engineering (1997), Stanford University
- B.S. Mathematics (1993), University of Science and Technology of China
Expertise
- Statistics
About
Xiaoming Huo is an A. Russell Chandler III Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. He also serves as the Associate Director for Research in the Institute for Data Engineering and Science. He served as a Program Director at the National Science Foundation (NSF) from 2013 to 2015. He received his Ph.D. in Statistics from Stanford University. He is a Fellow of the American Statistical Association (ASA).
Dr. Huo's research interests include statistical theory, statistical computing, and data analytics. He has made numerous contributions on topics such as sparse representation, wavelets, and statistical problems in detectability. His papers appeared in top journals, and some of them are highly cited. He is a senior member of IEEE. He was a Fellow of IPAM in September 2004. He won the Georgia Tech Sigma Xi Young Faculty Award in 2005.
He represented China in the 30th International Mathematical Olympiad (IMO), which was held in Braunschweig, Germany, in 1989, and received a gold prize.
Research
Dr. Huo’s research focuses on the intersection of statistics and data science, with particular emphasis on high-dimensional data analysis, statistical computing, and signal processing. His work addresses fundamental challenges in multiscale analysis, sparse representation, and the development of efficient algorithms for large-scale data. He has applied these methodologies to various domains, including biomedical imaging, cybersecurity, and financial modeling. His research has been supported by numerous grants from the National Science Foundation and the National Institutes of Health.
Teaching
My teaching philosophy focuses on bridging the gap between rigorous statistical theory and practical computational implementation to prepare students for the complexities of modern data science. Having taught a wide array of courses—including Regression Analysis (ISyE 6414), Time Series Analysis (ISyE 6402), Statistical Methods (ISyE 6739), and Data Mining (ISyE 7406)—I emphasize a "learning by doing" approach that integrates real-world case studies. A key contribution to the Georgia Tech curriculum is my development of the Computational Statistics (ISyE 6416) course, which equips students with the essential algorithmic tools needed for high-dimensional inference. Whether mentoring senior design teams or leading graduate seminars across the statistical spectrum, I am committed to fostering the technical proficiency and critical thinking necessary to translate mathematical concepts into impactful, data-driven solutions.
Awards and Honors
- Elected for Emerging Research Fronts in Mathematics 2006
- Georgia Tech Sigma Xi Young Faculty Award 2005
- Senior Member IEEE 2004
- Class of 1969 Teaching Fellow 2000
Representative Publications
- D.L. Donoho and X. Huo (2001), “Uncertainty principles and ideal atomic decomposition,” IEEE Transactions on Information Theory, 47 (7), 2845-2862.
- J. Chen and X. Huo (2006), “Theoretical results on sparse representations of multiple-measurement vectors,” IEEE Transactions on Signal Processing, 54 (12), 4634-4643.
- D.L. Donoho and X. Huo (2002), “Beamlets and multiscale image analysis,” Multiscale and Multiresolution Methods: Theory and Applications, 149-196.
- E. Arias-Castro, D.L. Donoho, and X. Huo (2005), “Near-optimal detection of geometric objects by fast multiscale methods,” IEEE Transactions on Information Theory, 51 (7), 2402-2425.
- X. Huo and G.J. Székely (2016), “Fast computing for distance covariance,” Technometrics, 58 (4), 435-447.
- C. Huang and X. Huo (2019), “A distributed one-step estimator,” Mathematical Programming, 174 (1), 41-76.
- M.K. Jeong, J.C. Lu, X. Huo, B. Vidakovic, and D. Chen (2006), “Wavelet-based data reduction techniques for process fault detection,” Technometrics, 48 (1), 26-40.
- Tian-Yi Zhou and Xiaoming Huo (2024). Classification of data generated by Gaussian mixture models using deep ReLU networks. Journal of Machine Learning Research, 25(190):1-54.
- Yiling Xie and Xiaoming Huo (2024). Adjusted Wasserstein distributionally robust estimator in statistical learning. Journal of Machine Learning Research, 25(148):1-40, 2024.