Computationally & Statistically Efficient Distributed Inference with Theoretical Guarantees

Dr. Xiaoming Huo is a professor at the Stewart School of Industrial & Systems Engineering at Georgia Tech. In this recording, he presents a web lecture titled, “Computationally & Statistically Efficient Distributed Inference.” Video Description In many contemporary data-analysis settings, it is expensive and/or infeasible to assume that the entire data set is available at a central location. In recent works of computational mathematics and machine learning, great strides have been made in distributed optimization and distributed learning (i.e., machine learning). On the other hand, classical statistical methodology, theory, and computation are typically based on the assumption that the entire data are available at a central location – this is a significant shortcoming in classical statistical knowledge. The statistical methodology and theory for distributed inference have been actively developed. This seminar will survey some new distributed statistical methods that are computationally efficient, requiring moderate communication, and have comparable statistical properties. Some theoretical guarantees of these distributed statistical estimators will be surveyed. The applicability and need of these methods can be found in a wide spectrum of application domains. They have potential impacts on healthcare, supply chain industries, retail and services, and many more. About the Speaker Dr. Xiaoming Huo is a professor at the Stewart School of Industrial & Systems Engineering at Georgia Tech. Dr. Huo's research interests include statistical theory, statistical computing, and issues related to data sciences. 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 since May 2004. He won the Georgia Tech Sigma Xi Young Faculty Award in 2005. His work has led to an interview by Emerging Research Fronts in June 2006 in the field of Mathematics - every two months, one paper is selected. Huo is a fellow of ASA and an AE for Technometrics. View slides from this lecture: https://drive.google.com/open?id=1vVkO0jzuZ2-da_AAPUFPo31p7rhetW_s Visit our webpage to view archived videos covering various topics in data science: https://bigdatau.ini.usc.edu/data-science-seminars