Recent Developments in Statistical Theory and Methods Based on Distributed Computing (18w5089)
Arriving in Oaxaca, Mexico Sunday, May 20 and departing Friday May 25, 2018
Sujit Ghosh (North Carolina State University)
Xiaoming Huo (Georgia Institute of Technology)
Hua Zhou (University of California, Los Angeles)
Mu Zhu (University of Waterloo)
This workshop will bring together researchers and practitioners who have recently worked to explore theory, algorithms, and applications of statistical procedures that are developed for distributed data and aggregated inference (i.e., distributed inference), with considerations on the storage, computational complexity, and statistical properties of the relevant estimators. The aim of this line of activities is to develop practical models, statistical theory, and computationally efficient and provably correct algorithms that can help scientists to conduct more effective distributed data analysis. Following up on a recent exploratory workshop at SAMSI (www.samsi.info) titled Distributed and Parallel Data Analysis (www.tinyurl.com/samsi-dpda-2016) we have gathered feedback from participants and we plan to organize a more focused workshop. Specific aims include (i) exposing academic researchers to both the challenges in industrial applications and current computing tools being used in industry, and (ii) introducing industrial researchers to the frontiers of mathematical statistics regarding distributed inference. The workshop will begin with few tutorial type lectures followed by lectures and panels and working groups on state-of-the-art research based methods by leading researchers and practitioners in this emerging field of mathematics and computer science.