Topic: Scalable Spatio-Temporal Regression and Deep Neural Networks
Topic: Scalable Spatio-Temporal Regression and Deep Neural Networks
Speaker: Dr. Fangfang Wang, Associate Professor, Department of Mathematical Sciences, Worcester Polytechnic Institute (WPI)
Abstract: Modern scientific studies often generate massive spatial and spatio-temporal datasets, creating new challenges for statistical modeling and computation. In this talk, I will discuss recent work on scalable methods for analyzing such data, with applications motivated by environmental and geospatial studies. The first part of the talk introduces a regression framework for large spatio-temporal datasets that accounts for spatial and temporal dependence while remaining computationally efficient for large-scale inference. The second part presents a deep learning approach for spatial regression that can capture complex nonlinear relationships between outcomes and covariates. Together, these methods illustrate how modern statistics and machine learning can be combined to address important challenges in large-scale spatial data analysis.
Biography: Dr. Wang is an Associate Professor of Statistics in the Department of Mathematical Sciences at Worcester Polytechnic Institute. She earned her B.S. in Mathematics from Nankai University, China, in 2004 and her Ph.D. in Statistics from the University of North Carolina at Chapel Hill in 2009, where she was advised by economist Prof. Eric Ghysels. Her research interests lie broadly in time series analysis, high-dimensional statistical inference, spatiotemporal modeling, derivative pricing, and the development of deep learning methods for complex data analysis.
The Seminars is open to the public free of charge.
*For further information, please contact Dr. Paul J. Gendron via email at pgendron@umassd.edu.
Lester W. Cory Conference Room, Science & Engineering Building (SENG), Room 213A
Paul J. Gendron
(508) 999-8510
pgendron@umassd.edu