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Hi! I'm Yuna

Education

  • 🌱 Aug. 2022 - May. 2024 : Computer Science - Specialization : Machine Learning @ Georgia Institute of Technology (MS)
  • 🌱 Mar. 2013 - Feb. 2018 : Library and Information Science & Journalism and Mass Communication (double major) @ SungKyunKwan university (BA)
    • ✈️ Aug. 2015 - Dec. 2015 : Informatics @ University of Washington (exchange student)
      • Got a full scholarship and living stipend as the first and only student to be selected from SKKU
      • Library assistant : Digitized Korean newspaper from 1980s to 1990s in accordance with east asia library's guideline

Experience

  • 💼 May. 2023 - Aug. 2023 : Data scientist @ WalMart Global Tech
  • 💼 Jan. 2018 - Aug. 2022 : Data analyst @ LG U+ - Managed churn rate
    • Jan. 2022 - Aug. 2022 : Customer Retention Strategy Team
    • Jan. 2021 - Dec. 2021 : Customer Retention Care Team
    • Jan. 2018 - Dec. 2020 : Mobile Customer Retention Team
    • Skills : Python, SQL, R
  • 💼 Jan. 2017 - Dec. 2017 : Attended a training program for senior students, called 'Early Bird', at LG U+
    • Internship at Business Convergent Service Team (B2B) for a month
    • Team Project 1 : Proposed the IoT service app to help parents' childcare, Presented the prototype with Arduino, 3D printing and the linked app
    • Team Project 2 : Presented a new pricing system and got 2nd place
  • 💼 Mar. 2016 - Dec. 2016 : Digital marketing manager @ Yellephant (kids app development company)
  • 💼 Feb. 2016 - Jul. 2016 : Internship @ Crenovationlink
    • Produced DMB and radio commercials (client : ohmycompany - crowd funcing site)

YUNA's Projects

acl-style-files icon acl-style-files

Official style files for papers submitted to venues of the Association for Computational Linguistics

classify_toxicity icon classify_toxicity

toxicity classifier using dataset : https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification

natural-questions icon natural-questions

Natural Questions (NQ) contains real user questions issued to Google search, and answers found from Wikipedia by annotators. NQ is designed for the training and evaluation of automatic question answering systems.

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