BEGIN:VCALENDAR VERSION:2.0 X-WR-CALNAME:EventsCalendar PRODID:-//hacksw/handcal//NONSGML v1.0//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York LAST-MODIFIED:20240422T053451Z TZURL:https://www.tzurl.org/zoneinfo-outlook/America/New_York X-LIC-LOCATION:America/New_York BEGIN:DAYLIGHT TZNAME:EDT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 DTSTART:19700308T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU END:DAYLIGHT BEGIN:STANDARD TZNAME:EST TZOFFSETFROM:-0400 TZOFFSETTO:-0500 DTSTART:19701101T020000 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT CATEGORIES:College of Engineering,Lectures and Seminars DESCRIPTION:Topic: Scalable Spatio-Temporal Regression and Deep Neural Netw orks Speaker: Dr. Fangfang Wang, Associate Professor, Department of Math ematical Sciences, Worcester Polytechnic Institute (WPI) Abstract: Moder n scientific studies often generate massive spatial and spatio-temporal da tasets, creating new challenges for statistical modeling and computation. In this talk, I will discuss recent work on scalable methods for analyzi ng such data, with applications motivated by environmental and geospatial studies. The first part of the talk introduces a regression framework fo r large spatio-temporal datasets that accounts for spatial and temporal de pendence while remaining computationally efficient for large-scale inferen ce. The second part presents a deep learning approach for spatial regressi on that can capture complex nonlinear relationships between outcomes and c ovariates. 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 Profess or of Statistics in the Department of Mathematical Sciences at Worcester P olytechnic Institute. She earned her B.S. in Mathematics from Nankai Uni versity, 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 ana lysis, high-dimensional statistical inference, spatiotemporal modeling, de rivative pricing, and the development of deep learning methods for complex data analysis. The Seminars is open to the public free of charge. *For fu rther information, please contact Dr. Paul J. Gendron via email at pgendro n@umassd.edu.\nEvent page: https://www.umassd.edu/events/cms/topic-scalabl e-spatio-temporal-regression-and-deep-neural-networks.php X-ALT-DESC;FMTTYPE=text/html:

Topic: Scalable Spatio-Temporal Regression and Deep Neural Networks

Speaker: Dr. Fangfang Wang\ , Associate Professor\, Department of Mathematical Sciences\, Worcester Po lytechnic Institute (WPI)

Abstract: Modern scientific studies of ten generate massive spatial and spatio-temporal datasets\, creating new c hallenges for statistical modeling and computation. In this talk\, I wil l discuss recent work on scalable methods for analyzing such data\, with a pplications motivated by environmental and geospatial studies. The first part of the talk introduces a regression framework for large spatio-tempo ral datasets that accounts for spatial and temporal dependence while remai ning 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. Togethe r\, these methods illustrate how modern statistics and machine learning ca n be combined to address important challenges in large-scale spatial data analysis.

Biography: Dr. Wang is an Associate Professor of Stati stics in the Department of Mathematical Sciences at Worcester Polytechnic Institute. She earned her B.S. in Mathematics from Nankai University\, C hina\, in 2004 and her Ph.D. in Statistics from the University of North Ca rolina at Chapel Hill in 2009\, where she was advised by economist Prof. E ric Ghysels. Her research interests lie broadly in time series analysis\, high-dimensional statistical inference\, spatiotemporal modeling\, derivat ive pricing\, and the development of deep learning methods for complex dat a analysis.

The Seminars is open to the public free of charge.

*For further information\, please contact Dr. Paul J. Gendron via ema il at pgendron@umassd.edu.

Event page: https://www.umassd.edu/events/cms/topic-scalable-spatio-tempor al-regression-and-deep-neural-networks.php

DTSTAMP:20260408T161020 DTSTART;TZID=America/New_York:20260409T140000 DTEND;TZID=America/New_York:20260409T150000 LOCATION:Lester W. Cory Conference Room, Science & Engineering Building (SENG), Room 213A SUMMARY;LANGUAGE=en-us:Topic: Scalable Spatio-Temporal Regression and Deep Neural Networks UID:76aface13165a3138d128e923e4336a4@www.umassd.edu END:VEVENT END:VCALENDAR