Kun Bu, Ph.D.

Kun Bu, Ph.D.

Computer Lab Assistant

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  • Timeline

  • About me

    Statistics Ph.D. | ML/NLP Researcher | Graduate Fellow at University of South Florida

  • Education

    • University of South Florida

      2018 - 2020
      Master of Arts - MA Statistics GPA 3.77
    • University of Tampa - John H. Sykes College of Business

      2015 - 2016
      Master of Science - MS Finance, General
    • University of South Florida

      2020 - 2024
      Doctor of Philosophy - PhD Statistics
    • East Tennessee State University

      2013 - 2015
      Bachelor of Business Administration (B.B.A.) Human Resources Management/Personnel Administration, General Major GPA 4.00
  • Experience

    • East Tennessee State University

      Feb 2014 - May 2015
      Computer Lab Assistant

      Provide students support and customer service, answer questions from students with technical issues, and prepared equipment for student and staff use.Maintain the front desk and assured the computer lab's rules and protocols are being followed.

    • University of South Florida

      Sept 2020 - now

      Research Area: Statistical Reinforcement Learning, Machine Learning, Natural Language Processing for social media,Biostatistics, Bayesian Data Analysis.Developed Multiple ensemble machine learning algorithms to conclude a better classifier and provided a robust classification method that improves the predicted performance while avoiding mode overfitting.Preformed statistical analysis in FDA Adverse Event Reporting System (FAERS) Public Database. Systematic analysis using R, PubMed API, and Python.Fine-tuned Microsoft ProphetNet model on text summarization to obtain one sentence summary of tweets and applied sentiment analysis in both NLTK and Transformer based model to compare the results before and after text summarization.Developed pipeline in extracting valid information, aggregated multiple similarity algorithms in detecting and gathering unique patients’ information from FDA FAERS Database.Built machine learning models and create association rule to analysis PDA/PubMed sources in drug-drug interaction.Apply RStan (a probabilistic programming language written in C++) to present a Bayesian method to analyze the ISO degradation data, which offers more straightforward implementation for estimating reliability and its associated uncertainty based on general ADT (Accelerated Degradation Test) data. Show less

      • Graduate Teaching Assistant

        Aug 2023 - now
      • Research Assistant

        Sept 2020 - Aug 2023
  • Licenses & Certifications