CV

A pdf version CV.

Education

• Ph.D. in Statistics, Texas A&M University, 2021 - Present.
  • Advisor: Dr. Anirban Bhattacharya.
  • GPA: 4.0 / 4.0
• Master in Mathematics, Texas A&M University, 2023 - Present.
  • Advisor: Dr. Jonathan W. Siegel. Co-working with Dr. Stephan Wojtowytsch.
• B.A in Economics and B.S. in Statistics, Seoul National Univeristy, 2013 - 2020.
  • Minor in Computer Science and Engineering.
  • Graduation with Honors (Summa Cum Laude).
  • Conducted Mandatory Military Service during the undergrad (2014 - 2016).
  • GPA (Overall, Major / Total): 3.96, 4.09 / 4.3

Publications

(* denotes equal contribution, alphabetically ordered authorship)

Published & Accepted Articles

• Jakwang Kim*, Jiyoung Park*, Anirban Bhattacharya (2025). “Robust Estimation in metric spaces: Achieving Exponential Concentration with a Fr'echet Median”. AISTATS 2025. Paper.
• Jiyoung Park, Ian Pelakh, Stephan Wojtowytsch (2023). “Minimum norm interpolation by perceptra: Explicit regularization and implicit bias”. NeurIPS 2023. Paper, Slides.

Preprints

• Jiyoung Park, Günay Doğan (2024). “Probabilistic U-Net with Kendall Shape Spaces for Geometry-Aware Segmentations of Images”. Arxiv.

Work experience

• NSF Math Sciences Graduate Internship.
  • National Institute of Standards and Technology (NIST), Gaithersburg (2023.05.22 - 2023.07.28).
  • Supervisor: Dr. Günay Doğan.
  • Topic: Geometric shape analysis.
• Research Resident, KC-ML2 (2019.05 - 2021.02).
  • Supervisor: Dr. Chan Y. Park.
  • Research in Graph Neural Network and Reinforcement Learning.
    • My blog post about Graph Neural Network and GraphDB.
• Software Engineer Intern, Naver Webtoon (2018.07 - 2018.08)
  • Implemented HBase API for log-data processing, and compared the performance with SQL based API codes.
• Undergrad Intern, Seoul National University.
  • Biostatistics Lab (2019.03 - 2019.08).
    • Autoencoder based Anomaly Detection Algorithm Project. Participated in Data processing.
    • Advisor: Dr. Myunghee Cho Paik.
  • Time Series and Predictive Analytics Lab (2018.12 - 2019.02).
    • RNN based Air Pollution Prediction.
    • Advisor: Dr. Sangyeol Lee.
  • Spatial Statistics Lab (2017.12 - 2018.02).
    • Hierarchical Bayesian Regression for Korean Schizophrenia data.
    • Advisor: Dr. Chae Young Lim.

Teaching

• Teaching Assistant
  • Stat 689: Special topics in Statistics-Advanced Bayes (Graduate) (Spring 2025)
    • Grading, Office hours.
  • Stat 633: Advanced Bayesian Modeling and Computation (Graduate) (Fall 2024)
    • Grading, Office hours.
  • Stat 438: Bayesian Statistics (Undergrad) (Spring 2024)
    • Grading, Writing solutions, Implementing Python codes for course materials.
  • Stat 445/645: Applied Biostatistics and Data Analysis (Master course) (Fall 2022).
    • Grading.
  • Stat 642: Methods of Stat II (Master course) (Spring 2022).
    • Grading.
  • Stat 652: Stat In Research II (Master course) (Fall 2021).
    • Implementing R codes for course materials.

Skills

• Programming Languages: Python, R, C, Java, C++, Assembly (x86-64).
• Libraries & Framework: Pytorch, Rcpp, Linux, Git, SQL, Latex, HBase.