BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//chikkutakku.com//RDFCal 1.0//EN
X-WR-CALDESC:GoogleカレンダーやiCalendar形式情報を共有シェ
 アしましょう。近所のイベントから全国のイベントま
 で今日のイベント検索やスケジュールを決めるならち
 っくたっく
X-WR-CALNAME:ちっくたっく
X-WR-TIMEZONE:UTC
BEGIN:VEVENT
SUMMARY:Alumni Lectures - Yannis Kevrekidis\, Johns Hopkins University\, 
 “No Equations\, No Variables\, No Parameters\, No Space and No Time: Dat
 a and the Modeling of Complex Systems\, Part I”
DTSTART;VALUE=DATE-TIME:20260924T153000Z
DTEND;VALUE=DATE-TIME:20260924T163000Z
UID:156066763768
DESCRIPTION:Host:  Dimitrios MaroudasThe Alumni Lectures this year will b
 e given by Yannis Kevrekidis\, Johns Hopkins University\, on Thursday\, Se
 ptember 24 and Friday\, September 25.  The first lecture on Thursday will
  be in LGRT 201.  The second lecture will be at 11:30 a.m. in S330-340 of
  the Life Science Laboratories\, with a reception directly following the s
 eminar.Yannis KevrekidisBloomberg Distinguished ProfessorApplied Mathemati
 cs and Statistics\, Chemical and Biomolecular Engineering &amp\; the M
 edical SchoolJohn Hopkins UniversityPomeroy and Betty Perry Smith Profes
 sor in Engineering\, EmeritusProfessor of Chemical and Biological Engine
 ering\, and of Applied and Computational Mathematics EmeritusPrinceton
  University“No Equations\, No Variables\, No Parameters\, No Space and
  No Time:Data and the Modeling of Complex Systems\, Part I”Thursday\,
  September 24\, 2026\, 11:30 a.m.LGRT 201&amp\; Zoom Virtual SeminarUMass 
 Amherst (Email wallace@ecs.umass.edu for Zoom Link)AbstractI will give an 
 overview of a research path in data driven modeling of complex systems ove
 r the last 35 or so years – from the early days of shallow neural networ
 ks and autoencoders for nonlinear dynamical system identification\, to t
 he more recent ML-assisted derivation of data driven “emergent” spaces
  in which to better learn generative PDE laws and accelerate their solutio
 n. In all illustrations presented\, I will try to point out connections be
 tween the “traditional” numerical analysis we know and love\, and 
 the more modern data-driven tools and techniques we now have – and s
 ome mathematical questions they hopefully make possible for us to answer.P
 art I will focus on results from the early 1990s (the last "AI winter") th
 at are now being resurrected and extended. BioYannis Kevrekidis studied C
 hemical Engineering at the National Technical University in Athens. He the
 n followed the steps of many alumni of that department to the University o
 f Minnesota\, where he studied with Rutherford Aris and Lanny Schmidt (a
 s well as Don Aronson and Dick McGehee in Math). He was a Director's Fel
 low at the Center for Nonlinear Studies in Los Alamos in 1985-86 (when Sov
 iets still existed and research funds were plentiful). He then had the g
 ood fortune of joining the faculty at Princeton\, where he taught Chemical
  Engineering and also Applied and Computational Mathematics for 31 yea
 rs\; eight years ago he became Emeritus and started fresh at Johns Hopki
 ns (where he somehow is also Professor of Urology). His work always had to
  do with nonlinear dynamics (from instabilities and bifurcation algorith
 ms to spatiotemporal patterns to data science in the 90s\, nonlinear ident
 ification\, multiscale modeling\, and back to data science/ML)\; and h
 e had the additional good fortune to work with several truly talented expe
 rimentalists\, like G. Ertl's group in Berlin. Currently -on leave from Ho
 pkins- he works with the Defense Sciences Office at DARPA. When young an
 d promising he was a Packard Fellow\, a Presidential Young Investigator 
 and the Ulam Scholar at Los Alamos National Laboratory. He holds the Colb
 urn\, CAST\,  Wilhelm and Walker awards of the AIChE\, the Crawford an
 d the Reid prizes of SIAM\, he is a member of the NAE\, the American Acade
 my of Arts and Sciences\, and the Academy of Athens. 
LOCATION:LGRT 201 and via Zoom (email Marie for a link\, wallace@ecs.umass
 .edu)
END:VEVENT
END:VCALENDAR