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Timothy Jurka

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Timothy Jurka
Alma materUniversity of California, Davis
Known forContributions to document classification software
Scientific career
FieldsComputer Science
Political Science

Timothy Jurka is a Polish-American computer scientist and political scientist.

Background

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Jurka is best known for developing the artificial intelligence that ranks the LinkedIn news feed.[1][2] Previously, Jurka developed machine learning algorithms for news recommendations in the Pulse news reading application, which was acquired by LinkedIn in 2013.[3]

As a Ph.D. student at UC Davis, Jurka collaborated on numerous projects in political science spanning media framing,[4][5] civic engagement,[6] and tobacco and immunization policy.[7] Additionally, he wrote text classification software, including RTextTools and MaxEnt for the R statistical programming language.[8][9][10][11]

He is the son of computational biologist Jerzy Jurka.[12]

References

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  1. ^ LinkedIn "A Look Behind the AI that Powers LinkedIn’s Feed"
  2. ^ Axios "LinkedIn goes niche"
  3. ^ TechCrunch "LinkedIn Acquires Pulse For $90M In Stock And Cash"
  4. ^ University of Chicago Press "Making the News: Politics, the Media, and Agenda Setting"
  5. ^ Washington Monthly "College Students on the Debate: Agreeing with Obama, Agreeing that Romney Won"
  6. ^ Social Science Research Network "Colleague Crowdsourcing: A Method for Incentivizing National Student Engagement and Large-N Data Collection"
  7. ^ State Politics and Policy Conference "Agendas and Alternatives in the American States: Determinants of State Legislative Attention to Tobacco and Immunizations"
  8. ^ Google Scholar "Timothy P. Jurka"
  9. ^ The R Journal "RTextTools: A Supervised Learning Package for Text Classification"
  10. ^ The R Journal "maxent: An R Package for Low-memory Multinomial Logistic Regression with Support for Semi-automated Text Classification"
  11. ^ DataScience+ "Sentiment analysis with machine learning in R"
  12. ^ Jurka, E. (2015). "Jerzy Jurka: June 4, 1950 – July 19, 2014". Mob DNA. 6: 2. doi:10.1186/s13100-014-0032-2. PMC 4293820.
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