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:Modern scientific studies often generate massive spatial and sp atio-temporal datasets, creating new challenges for statistical modeling a nd computation. In this talk, I will discuss recent work on scalable metho ds for analyzing such data, with applications motivated by environmental a nd 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-s cale inference. The second part presents a deep learning approach for spat ial regression that can capture complex nonlinear relationships between ou tcomes and covariates. Together, these methods illustrate how modern stati stics and machine learning can be combined to address important challenges in large-scale spatial data analysis.\nEvent page: https://www.umassd.edu /events/cms/joint-ece-and-dsc-seminar-scalable-spatio-temporal-regression- and-deep-neural-network.php X-ALT-DESC;FMTTYPE=text/html:

Modern scientific studies often generate massive spatial and spatio-temporal datasets\, creating new chal lenges for statistical modeling and computation. In this talk\, I will dis cuss recent work on scalable methods for analyzing such data\, with applic ations motivated by environmental and geospatial studies. The first part o f the talk introduces a regression framework for large spatio-temporal dat asets that accounts for spatial and temporal dependence while remaining co mputationally efficient for large-scale inference. The second part present s 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 comb ined to address important challenges in large-scale spatial data analysis.

Event page: https://www.umassd.edu/events/cms/joint-ece-and-dsc-seminar-scalable- spatio-temporal-regression-and-deep-neural-network.php

< /html> DTSTAMP:20260409T164109 DTSTART;TZID=America/New_York:20260409T140000 DTEND;TZID=America/New_York:20260409T150000 LOCATION:SENG 213A SUMMARY;LANGUAGE=en-us:Joint ECE and DSC Seminar: Scalable Spatio-Temporal Regression and Deep Neural Network UID:9b67c23f30e0a92ce7d780bf57012f20@www.umassd.edu END:VEVENT END:VCALENDAR