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UID:271@escience.washington.edu
DTSTART;TZID=America/Los_Angeles:20250528T163000
DTEND;TZID=America/Los_Angeles:20250528T172000
DTSTAMP:20250528T190613Z
URL:https://escience.washington.edu/events/uw-data-science-seminar-donsub-
 rim/
SUMMARY:UW Data Science Seminar: Donsub Rim
DESCRIPTION:Please join us for a UW Data Science Seminar featuring Donsub R
 im from the Mathematics Department at Washington University in St. Louis o
 n  Wednesday\, May 28th from 4:30 to 5:20 p.m. PT. The seminar will be hel
 d in Electrical and Computer Engineering Building 125 – Campus Map.\n"A
  Stability Analysis of Neural Networks and Its Applications to Tsunami Ear
 ly Warning"\n\nAbstract:  Feedforward neural networks (NNs) have remarkabl
 e potential to serve as fast and accurate prediction models in many high-c
 onsequence applications. However\, current NN models are not yet suitable 
 for deployment in practice\, since they are known to be unstable with resp
 ect to input perturbations called adversarial examples. The focus of this 
 talk is in analyzing these instabilities. We will introduce low rank House
 holder expansion (LRHE) of NNs\, a certain linearization of the NN about a
 n input\, and discuss computational experiments illustrating a close relat
 ionship between the low-rank structure revealed by LRHE and the adversaria
 l examples. Throughout this talk\, we will focus on a particular geophysic
 al application in tsunami early warning\, namely NN models that aim to pre
 dict tsunami waveforms solely from geodetic measurements of the earthquake
 .\n\nThis is a joint work with Sanah Suri (Wash U)\, Sanghyun Hong (Oregon
  State U)\, Kookjin Lee (Arizona State U)\, and Randall J. LeVeque (U Wash
 ington).\n\n  Biography:  Donsub Rim is an Assistant Professor of Mathemat
 ics in the Mathematics Department at Washington University in St. Louis. H
 e received his PhD at the University of Washington\, and held postdoctoral
  positions at Columbia University and New York University. His current res
 earch interests are in dimensionality reduction of solutions of nonlinear 
 wave equations\, analysis of neural networks and tsunami early warning.\n\
 nThe 2024-2025 seminars will be held in person\, and are free and open to 
 the public.
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