About

I am an Associate Professor in the Department of Statistics at Korea University. My research interests include dimension reduction, functional data analysis, statistical machine learning, data privacy, synthetic data generation, and various machine learning problems.

Prior to joining KU in 2021, I worked at the University of North Carolina at Charlotte as an Assistant Professor of Statistics and an Affiliated Faculty in the School of Data Science. I received a Ph.D. in Statistics at The Pennsylvania State University, and a B.S. in Statistics and a B.S. in Mathematical Sciences at Seoul National University in South Korea.

Department of Statistics, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, South Korea

Email: junsong@korea.ac.kr · My CV

Last updated in September 2026.

Biography

Education

  • The Pennsylvania State University
    Ph.D. in Statistics · Advisor: Professor Bing Li
    2017
  • Seoul National University
    B.S. in Statistics · B.S. in Mathematical Sciences
    2011

Academic Positions

  • Korea University
    Associate Professor, Department of Statistics (September 2022 – Present)
    Assistant Professor, Department of Statistics (September 2021 – August 2022)
    2021 – Present
  • University of North Carolina at Charlotte
    Assistant Professor, Department of Mathematics and Statistics
    Affiliated Faculty, School of Data Science (from August 2020)
    2017 – 2021

Grants & Awards

  • National Research Foundation of Korea, Basic Research Lab (기초연구실), PI, 2026–2029
  • National Research Foundation of Korea, Pilot and Feasibility Grant (개척연구), sole PI, 2025–2028
  • National Research Foundation of Korea, Basic Research Lab (기초연구실), co-I, 2023–2026
  • Ministry of Science and ICT of Korea, Developing Convergence Talent in Data Science, co-I, 2022–2028
  • National Research Foundation of Korea, Young Scientist Grants (Outstanding Track, 우수신진연구), sole PI, 2022–2027
  • UNC Charlotte, Faculty Research Grant Award, sole PI, 2018–2019
  • Penn State, William Harkness Teaching Award, 2016
  • Penn State, Jack and Eleanor Pettit Scholarship in Science, 2016
  • Penn State, William Harkness Travel Award for JSM Seattle, 2015
  • Penn State, August and Ruth Homeyer Graduate Fellowship, 2012–2013
  • Penn State, University Graduate Fellowship, 2012–2013
  • Seoul National University, Brain Korea 21 Fellowship, 2011–2012
  • Korea Science and Engineering Foundation, National Science and Technology Scholarship, Korea, 2004–2010

Research

Research Interests

  • Sufficient Dimension Reduction
  • Functional Data Analysis
  • Data Privacy & Synthetic Data Generation
  • Federated Learning
  • Statistical Machine Learning & Deep Learning

Publications

† Graduate student supervised · * Corresponding author

  1. Sufficient Dimension Reduction for the Conditional Quantiles of Functional Data
    Eliana Christou, Eftychia Solea, Shanshan Wang, and Jun Song*
    Statistica Sinica, accepted (2027)
    DOIPDF
  2. Nonlinear Sufficient Dimension Reduction for Conditional Quantiles in Scalar-on-Function Single-Index Models
    Shanshan Wang, Eliana Christou, Eftychia Solea, and Jun Song*
    Statistics and Computing, 36(1), 32 (2026)
    DOI
  3. Valid Asymptotic Inference after Sufficient Dimension Reduction in a Single-Index Framework
    Kyongwon Kim, Jun Song*, and Jae Keun Yoo
    Statistics and Computing, 36(1), 24 (2026)
    DOI
  4. Robust Inverse Regression for Multivariate Elliptical Functional Data
    Eftychia Solea, Eliana Christou, and Jun Song*
    Statistica Sinica, 36, 1023–1042 (2026)
    DOI
  5. Functional Adaptive Group Lasso with Its Non-asymptotic Bounds
    Sehun Jang† and Jun Song*
    Electronic Journal of Statistics, 19(2), 3927–3954 (2025)
    DOICode
  6. Dimension Reduction for the Conditional Quantiles of Functional Data with Categorical Predictors
    Shanshan Wang, Eliana Christou, Eftychia Solea, and Jun Song*
    Biometrical Journal, 67(6), e70102 (2025)
    DOI
  7. Enhancing Sufficient Dimension Reduction via Hellinger Correlation
    SeungBeom Hong†, Ilmun Kim, and Jun Song*
    Proceedings of the 41st International Conference on Machine Learning (ICML 2024), PMLR 235:18634–18647 (2024)
    LinkarXivCode
  8. A Selective Review of Nonlinear Sufficient Dimension Reduction
    Sehun Jang† and Jun Song*
    Communications for Statistical Applications and Methods, 31(2), 247–262 (2024)
    DOI
  9. On a Nonlinear Extension of the Principal Fitted Component Model
    Jun Song, Kyongwon Kim, and Jae Keun Yoo
    Computational Statistics and Data Analysis, 182, 107707 (2023)
    DOI
  10. A Novel Approach to Characterize State-level Food Environment and Predict Obesity Rate Using Social Media Data: Correlational Study
    Chuqin Li, Alexis Jordan, Jun Song, Yaorong Ge, and Albert Park
    Journal of Medical Internet Research, 24(12), e39340 (2022)
    DOI
  11. Multivariate Functional Group Sparse Regression: Functional Predictor Selection
    Ali Mahzarnia† and Jun Song*
    PLoS ONE, 17(4), e0265940 (2022)
    DOIarXivCode
  12. Dimension Reduction for Functional Data Based on Weak Conditional Moments
    Bing Li and Jun Song
    The Annals of Statistics, 50(1), 107–128 (2022)
    DOI
  13. Sparse Multivariate Functional Principal Component Analysis
    Jun Song and Kyongwon Kim
    Stat, 11(1), e435 (2022)
    DOI
  14. Multivariate Neighborhood Trajectory Analysis: An Exploration of the Functional Data Analysis Approach
    Paul H. Jung† and Jun Song*
    Geographical Analysis, 54(4), 789–819 (2022)
    DOI
  15. Nonlinear and Additive Principal Component Analysis for Functional Data
    Jun Song* and Bing Li
    Journal of Multivariate Analysis, 181, 104675 (2021)
    DOICode
  16. On Sufficient Dimension Reduction for Functional Data: Inverse Moment Based Methods
    Jun Song*
    Wiley Interdisciplinary Reviews: Computational Statistics, 11(4), e1459 (2019)
    DOI
  17. Molecular, Physiological and Behavioral Responses of Honey Bee (Apis mellifera) Drones to Infection with Microsporidian Parasites
    Holly Holt, Gabriel Villar, Weiyi Cheng, Jun Song, and Christina Grozinger
    Journal of Invertebrate Pathology, 155, 14–24 (2018)
    DOI
  18. Nonlinear Sufficient Dimension Reduction for Functional Data
    Bing Li and Jun Song
    The Annals of Statistics, 45(3), 1059–1095 (2017)
    DOICode

Under Review

  1. Interaction-Aware Nonlinear Scalar-on-Function Regression with Functional Predictor Selection via Adaptive RKHS
    Sehun Jang† and Jun Song*
    Submitted

Teaching

Korea University

  • DAS 512Principles of Statistical Learning IISummer 2024, Fall 2025, Fall 2026
  • DAS 511Principles of Statistical Learning ISpring 2024, Summer 2025, Summer 2026
  • DAS 507Mathematical Foundation for Data ScienceSpring 2026
  • STA 829Topics in Applied Statistics I (Functional Data Analysis)Fall 2023, Fall 2026
  • STA 617Advanced Statistical Machine LearningFall 2021, Spring 2025, Spring 2026
  • STA 518Statistical Methodology for Data AnalysisFall 2022
  • STAT 424Statistical Machine LearningFall 2023, Fall 2025, Fall 2026
  • STAT 433Statistical Modeling for Deep LearningSpring 2022
  • STAT 221Introduction to Probability TheorySpring 2022, Fall 2022, Spring 2023, Spring 2024, Spring 2025, Spring 2026
  • STAT 232Mathematical StatisticsFall 2021
  • Workshop (8 hours) — R을 이용한 기초 통계학Feb. 2022, Summer 2022
  • Workshop (8 hours) — 로지스틱 회귀분석Summer 2022
  • Coding Bootcamp (10 hours) — R & Python for Data ScienceFeb. 2023, Feb. 2024

UNC Charlotte

  • STAT 7133/8133Multivariate AnalysisSpring 2018, Spring 2019
  • STAT 6115/DSBA 6115Statistical Learning with Big DataFall 2018, Fall 2019, Fall 2020
  • STAT 3122/MATH 3122Probability and Statistics ISpring 2019
  • STAT 2122Introduction to Probability and StatisticsSpring 2020, Fall 2020, Spring 2021
  • STAT 1222Introduction to StatisticsFall 2017, Summer 2018, Summer 2019
  • STAT 1221Elements of Statistics IFall 2017, Fall 2018, Fall 2019, Spring 2020

Penn State

  • STAT 414/MATH 414Introduction to Probability TheorySummer 2015, Summer 2016
  • STAT 318/MATH 318Elementary ProbabilityFall 2015, Spring 2016

Students

Current Students

Ph.D.

  • Sehun Jang (장세훈)sahoon1004@korea.ac.kr
  • Dabeen Kim (김다빈)antl2047@korea.ac.kr

M.S.

  • Kibum Kim (김기범)saw9090@korea.ac.kron leave · @Deep-AI
  • Joonseo Kwon (권준서)jkings97@korea.ac.kr
  • Saeyon Lee (이세연)yeony09@korea.ac.kr
  • Kyeongjoo Song (송경주)kjsong0506@korea.ac.kr
  • Eojin Lee (이어진)eojin1245@korea.ac.kr
  • Yerhin Cho (조예린)y0605fre@korea.ac.kr
  • Seonghoon Jhang (장성훈)sunnyjhang@korea.ac.kr

Former Students

at Korea University

  • Minah Kim (김민아) — M.S. in August 2026
  • Siho Yun (윤시호) — M.S. in August 2026 · PwC
  • Seung Won Chung (정승원) — M.S. in February 2026 Thesis: DANGR: Domain-Adversarial Noise Generation for Robust Classification
  • Noa Jeong (정노아) — M.S. in February 2026 · Korea Development Bank (한국산업은행) Thesis: A Unified Statistical Framework for Semi-supervised Anomaly Detection
  • Jiho Choi (최지호) — M.S. in February 2026 Thesis: A Novel Approach Leveraging Quadratic Basis Nodes and TabPFN
  • Nahyun Han (한나현) — M.S. in February 2026 · Kearney Thesis: Deep Nonlinear Sufficient Dimension Reduction via Density Ratio Learning
  • Yerim An (안예림) — M.S. in August 2025 · PwC Consulting Thesis: Integrating Self-Attention into MLP-Based Architectures for Improved Multivariate Time Series Forecasting
  • Hwijin Seo (서휘진) — M.S. in August 2025 Thesis: Extracting Multi-Index Structures from Single-Index Hellinger Correlation Based Sufficient Dimension Reduction
  • Donghoon Lee (이동훈) — M.S. in February 2025 · Samsung SDS Thesis: Functional Data Synthesis Using Adaptive Basis and Latent Space Diffusion in VAE
  • Sumin Jeon (전수민) — M.S. in February 2025 · SK AX Thesis: Improving Multivariate Time Series Forecasting Accuracy with Weighted PatchTST for Further Prediction
  • Dongcheol Shin (신동철) — M.S. in August 2024 · PwC Consulting Thesis: A Unified Framework for Synthetic Data Generation of Tabular Data via Self-Attention
  • Dong Hyun Kang (강동현) — M.S. in August 2024 · Korea Securities Finance (한국증권금융) Thesis: Synthetic Data Generation via Distorted Principal Loadings: Application to Data Integration
  • Yujin Hwang (황유진) — M.S. in August 2024 · Pennsylvania State University (Statistics Ph.D. student) Thesis: Nonparametric Variable Selection for Mixed Model
  • SeungBeom Hong (홍승범) — M.S. in February 2024 · Korea Insurance Development Institute (보험개발원) Thesis: Enhancing Sufficient Dimension Reduction via Hellinger Correlation
  • Dongkwun Yoo (유동균) — M.S. in February 2024 · Lotte Insurance Thesis: Progressive Folded-MAVE
  • Younghun Ko (고영헌) — M.S. in February 2024 · EY Hanyoung (EY한영) Thesis: Penalized Neural Network Sufficient Dimension Reduction

at UNC Charlotte

  • Ali Mahzarnia — Ph.D. in 2021 First job: Postdoctoral Researcher at Duke University, now at Stanford University
  • Tanmay Kenjale — B.S. Honor’s thesis in 2021
  • Avery Johnson — M.S. in 2021
  • Ruari Swift-christian — B.S. in 2020
  • Wei Zhang — M.S. in 2018

Other Experiences

  • Pennsylvania State University
    Graduate Assistant, Department of Statistics
    2013 – 2017
  • Seoul National University
    Graduate Assistant, Department of Statistics
    2011 – 2012
  • Bank of Korea
    Research Assistant
    Oct – Nov 2008
  • Boston Consulting Group
    Research Assistant
    Aug – Sep, Dec 2008
  • Republic of Korea Army
    Sergeant (Mandatory Military Service)
    2005 – 2007