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BEGIN:VEVENT

CATEGORIES:Engineering
CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Panel/Seminar/Colloquium
CATEGORIES:Conference/Symposium
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=8a0290b4-860465b2-0186-32f30301-00004779:Rice\, Cyn
 thia
CREATED:20240307T154521Z
DESCRIPTION:Title: Codes\, nearest neighbors\, and learning: From communic
 ation to computation\nAbstract:   In this talk\, I will present from an i
 nformation and coding theoretic view\, how information can be represented
  for purposes of reliable communication and efficient computation and lea
 rning. The problems I will discuss are connected through the concept of n
 earest neighbors\, which is a basic decoding rule for error correction in
  data communication\, and a widely used method for data classification an
 d regression. To begin with\, I will talk about the construction of codes
  correcting deletion/insertion errors\, which is a longstanding open prob
 lem and has a wide range of applications in communication and storage sys
 tems. Following this\, I will show how our coding theoretic results on de
 letion/insertion-correcting codes can be applied in the statistical learn
 ing problem of trace reconstruction\, where the goal is to reconstruct a 
 sequence from multiple noisy samples of that sequence. The trace reconstr
 uction problem has applications in genomic data processing and can be con
 sidered as learning with multiple neighbors. Next\, I will view nearest n
 eighbors as a model of computation based on a labeled dataset and discuss
  how the labeled dataset can be compressed under nearest neighbor rules t
 o make the computation more efficient. The nearest neighbor model has app
 lications in vector databases for large language models. If time permits\
 , I will talk about theoretical limits and algorithms for privacy-preserv
 ing learning. \n\nBio:  Jin Sima is a postdoctoral researcher in the Depa
 rtment of Electrical and Computer Engineering at University of Illinois U
 rbana-Champaign. He received a B.Eng. and a M.Sc. in Electronic Engineeri
 ng from Tsinghua University\, China\, in 2013 and 2016 respectively\, and
  a Ph.D in Electrical Engineering from California Institute of Technology
  (Caltech) in 2022. His research interests include information and coding
  theory\, machine learning\, and theory of computation. He is a recipient
  of the 2019 IEEE Jack Keil Wolf ISIT Student Paper Award\, the 2020-2021
  IEEE Communication Society Data Storage Best Paper Award\, the 2022 Calt
 ech Charles Wilts Prize for outstanding doctoral thesis in Electrical Eng
 ineering\, and the 2023 IEEE Information Theory Society Thomas M. Cover D
 issertation Award.
DURATION:PT1H
DTSTAMP:20240312T171452Z
DTSTART;TZID=America/New_York:20240318T120000
LAST-MODIFIED:20240312T171452Z
LOCATION;X-BEDEWORK-UID=18832edc-1b287efb-011b-2c10ff6a-00000003:Teer 203
STATUS:CONFIRMED
SUMMARY:ECE Seminar - Jin Sima
UID:CAL-8a0292fd-8d13410f-018e-19972bc7-00001491demobedework@mysite.edu
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 er/public-user/Lectures_Conferences/Panel_Seminar_Colloquium
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Engineering:/user/public-use
 r/Topics/Engineering
X-BEDEWORK-SUBMITTEDBY:cpr24 for Electrical and Computer Engineering (ECE)
  (agrp_PrattSchool_ECE)
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X-BEDEWORK-IMAGE-ALT-TEXT:Jin Sima
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END:VEVENT
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