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UID:33@escience.washington.edu
DTSTART;TZID=America/Los_Angeles:20230517T163000
DTEND;TZID=America/Los_Angeles:20230517T172000
DTSTAMP:20230503T170312Z
URL:https://escience.washington.edu/events/uwdss-flaxman/
SUMMARY:UW Data Science Seminar: Abraham Flaxman
DESCRIPTION:\n\nPlease join us for a UW Data Science Seminar event on Wedn
 esday\, May 17th from 4:30 to 5:20 p.m. PST. The seminar will feature Abra
 ham D. Flaxman\, Associate Professor of Health Metrics Sciences at the UW 
 Institute for Health Metrics and Evaluation (IHME).\n\n\nUse this zoom lin
 k to join\n&nbsp\;\n"Introducing pseudopeople: census-scale simulated data
  for entity resolution"\n\nAbstract: I will introduce and demo pseudopeop
 le\, our new\, publicly available Python package that we hope you will use
  in entity resolution research and development. pseudopeople generates c
 ensus-scale\, simulated population data with adjustable parameters\, to re
 plicate key complexities from real challenges in record linkage work. Typi
 cal applications of entity resolution and record linkage rely on sensitive
  and confidential data\, and this can be a barrier to reproducible computa
 tional research and sometimes even to open communication about innovations
  and challenges. The value hypothesis of this work is that creating realis
 tic\, simulated data (that includes non-confidential simulated versions of
  sensitive fields\, like name\, address\, and date of birth) will enable m
 ore research in census-scale entity resolution and guide the research towa
 rds challenges that Census Bureau faces in practice.\nOur work builds on p
 revious entity resolution data projects\, such as FEBRL\, GeCO\, and SOG\,
  as well as our microsimulation framework\, Vivarium. We model individual 
 people and their household\, family\, and employment relations at USA scal
 e\, and include simulated versions of confidential attributes like name\, 
 address\, income\, and social security number. On top of this\, we simulat
 ed a range of census-relevant data collection mechanisms\, including simul
 ated decennial censuses\, simulated ACS and CPS surveys\, simulated tax re
 cords\, and simulated social security administrative data. By creating re
 alistic\, but non-confidential\, data which includes these attributes\, we
  can make entity resolution research and development easier for ourselves 
 and others.\nBiography: Abraham D. Flaxman\, PhD\, is an Associate Profess
 or of Health Metrics Sciences at the Institute for Health Metrics and Eval
 uation (IHME) at the University of Washington. He is currently leading the
  development of a simulation platform to derive “what-if” results from
  Global Burden of Disease estimates and is engaged in software engineering
  and development for verbal autopsy and probabilistic record linkage. Dr. 
 Flaxman has previously designed software tools such as DisMod-MR that IHME
  uses to estimate the Global Burden of Disease\, and the Bednet Stock-and-
 Flow Model\, which has produced estimates of insecticide-treated net cover
 age in sub-Saharan Africa.\nThe UW Data Science Seminar is an annual lect
 ure series at the University of Washington that hosts scholars working acr
 oss applied areas of data science\, such as the sciences\, engineering\, h
 umanities and arts along with methodological areas in data science\, such 
 as computer science\, applied math and statistics. Our presenters come fro
 m all domain fields and include occasional external speakers from regional
  partners\, governmental agencies and industry.\nThe 2022-2023 seminars wi
 ll be virtual\, and are free and open to the public.
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