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 II”
DTSTART;VALUE=DATE-TIME:20260925T153000Z
DTEND;VALUE=DATE-TIME:20260925T163000Z
UID:203175779281
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 II”Friday\, 
 September 25\, 2026\, 11:30 a.m.S330-340 Life Science Laboratories&amp\; Z
 oom Virtual SeminarUMass Amherst (Email wallace@ecs.umass.edu for Zoom Lin
 k)AbstractI will give an overview of a research path in data driven modeli
 ng of complex systems over the last 35 or so years – from the early days
  of shallow neural networks and autoencoders for nonlinear dynamical sys
 tem identification\, to the more recent ML-assisted derivation of data dri
 ven “emergent” spaces in which to better learn generative PDE laws and
  accelerate their solution. In all illustrations presented\, I will try to
  point out connections between the “traditional” numerical analysis we
  know and love\, and the more modern data-driven tools and technique
 s we now have – and some mathematical questions they hopefully make po
 ssible for us to answer. Part II will focus more on contributions from th
 e data science side\, and on mathematical modeling questions whose study h
 as been enabled precisely because of recent AI software and hardware devel
 opments.BioYannis Kevrekidis studied Chemical Engineering at the National 
 Technical University in Athens. He then followed the steps of many alumni 
 of that department to the University of Minnesota\, where he studied with 
 Rutherford Aris and Lanny Schmidt (as well as Don Aronson and Dick McG
 ehee in Math). He was a Director's Fellow at the Center for Nonlinear Stud
 ies in Los Alamos in 1985-86 (when Soviets still existed and research fu
 nds were plentiful). He then had the good fortune of joining the faculty a
 t Princeton\, where he taught Chemical Engineering and also Applied and
  Computational Mathematics for 31 years\; eight years ago he became Emeri
 tus and started fresh at Johns Hopkins (where he somehow is also Profess
 or of Urology). His work always had to do with nonlinear dynamics (from in
 stabilities and bifurcation algorithms to spatiotemporal patterns to dat
 a science in the 90s\, nonlinear identification\, multiscale modeling\, a
 nd back to data science/ML)\; and he had the additional good fortune to
  work with several truly talented experimentalists\, like G. Ertl's group 
 in Berlin. Currently -on leave from Hopkins- he works with the Defense Sc
 iences Office at DARPA. When young and promising he was a Packard Fellow
 \, a Presidential Young Investigator and the Ulam Scholar at Los Alamos 
 National Laboratory. He holds the Colburn\, CAST\,  Wilhelm and Walker 
 awards of the AIChE\, the Crawford and the Reid prizes of SIAM\, he is a 
 member of the NAE\, the American Academy of Arts and Sciences\, and th
 e Academy of Athens. \n\nGoogle Meet に参加: https://meet.google.com/c
 ns-fedn-fxu\n\nMeet の詳細: https://support.google.com/a/users/answer/9
 282720
LOCATION:S330-340 Life Science Laboratories and via Zoom (email Marie for 
 a link\, wallace@ecs.umass.edu)
END:VEVENT
END:VCALENDAR