BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Department of Applied Mathematics and Statistics - ECPv6.5.0.1//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Department of Applied Mathematics and Statistics
X-ORIGINAL-URL:https://engineering.jhu.edu/ams
X-WR-CALDESC:Events for Department of Applied Mathematics and Statistics
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20220313T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20221106T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220331T133000
DTEND;TZID=America/New_York:20220331T143000
DTSTAMP:20240611T081512
CREATED:20220120T175509Z
LAST-MODIFIED:20230228T174156Z
UID:39320-1648733400-1648737000@engineering.jhu.edu
SUMMARY:AMS Weekly Seminar w/ Jose Perea (Northeastern University) @ Krieger 205 or on Zoom
DESCRIPTION:Title: DREiMac: Dimensionality Reduction with Eilenberg-MacLane Coordinates \nAbstract: Dimensionality reduction is the machine learning problem of taking a data set whose elements are described with potentially many features (e.g.\, the pixels in an image)\, and computing representations which are as economical as possible (i.e.\, with few coordinates). In this talk\, I will present a framework to leverage the topological structure of data (measured via persistent cohomology) and construct low dimensional coordinates in (classifying) spaces consistent with the underlying data topology. \nHere is the zoom link is: https://wse.zoom.us/j/95448608570
URL:https://engineering.jhu.edu/ams/event/ams-weekly-seminar-w-jose-perea-northeastern-university-maryland-110-or-on-zoom/
CATEGORIES:Seminars and Lectures
END:VEVENT
END:VCALENDAR