BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Penn Engineering Events - ECPv6.15.18//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Penn Engineering Events
X-ORIGINAL-URL:https://seasevents.nmsdev7.com
X-WR-CALDESC:Events for Penn Engineering Events
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:20210314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20211107T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20220313T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20221106T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20230312T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20231105T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221115T120000
DTEND;TZID=America/New_York:20221115T133000
DTSTAMP:20260405T121858
CREATED:20221111T214536Z
LAST-MODIFIED:20221111T214536Z
UID:7863-1668513600-1668519000@seasevents.nmsdev7.com
SUMMARY:ESE Ph.D. Thesis Defense - "Learning and Control of Network Phenomena"
DESCRIPTION:The intersection of dynamical systems and networks are used to model a huge variety of phenomena such as the spread of disease\, multi-agent systems\, opinions in social networks\, and more. Many properties of these network phenomena can be understood by examining the eigenvalue spectrum of a matrix representation of the underlying graph. Using this intuition\, this thesis explores the learning and control of network phenomena. First\, I present techniques for matching individuals across correlated networks and learning the spectra of a graph matrix using only the sparse output measurements of a networked dynamical system with periodic inputs. Next\, I present a data-driven framework for multi-task learning and non-linear control of epidemics. Finally\, I propose a new architecture for signal processing on higher-order graphs\, along with a new transferability bound on the performance of graph neural networks via spectral similarity. This transferability result is valid for arbitrary graphs regardless of their structure\, resulting in the first bound on the transferability of a machine learning approach for higher-order graphs.
URL:https://seasevents.nmsdev7.com/event/ese-ph-d-thesis-defense-learning-and-control-of-network-phenomena/
LOCATION:Room 401B\, 3401 Walnut\, 3401 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Dissertation or Thesis Defense
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
END:VCALENDAR