Jun Song

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, 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, B.S. in Statistics and B.S. in Mathematical Sciences at Seoul National University in South Korea.

Office: 424 CPSE Bldg. (정경관 424)

My CV.

Last updated in November, 2025.

Education

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Academic Position

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Grants & Awards

• 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
• National Research Foundation of Korea, Developing Convergence Talent in Data Science, co-I, 2022-2029
• 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 Interests

• Sufficient Dimension Reduction
• Functional Data Analysis
• High-dimensional Problems
• Privacy and Synthetic Data Generation
• Statistical Machine Learning & Deep Learning

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Publications

^†: Graduate student, *: Corresponding author

• Shanshan Wang, Eliana Christou, Eftychia Solea, and Jun Song* (2026)

  Nonlinear Sufficient Dimension Reduction for Conditional Quantiles in Scalar-on-Function Single-Index Models

  Statistics and Computing. Accepted.
• Kyongwon Kim, Jun Song*, and Jae Keun Yoo. (2026)

  Valid asymptotic inference after sufficient dimension reduction in a single-index framework

  Statistics and Computing. Accepted.
• Sehun Jang^† and Jun Song* (2025)

  Functional adaptive group lasso with its non-asymptotic bounds

  Electronic Journal of Statistics, 19(2): 3927-3954. [Link] [Code]
• Shanshan Wang, Eliana Christou, Eftychia Solea, and Jun Song* (2025+)

  Dimension Reduction for the Conditional Quantiles of Functional Data with Categorical Predictors

  Biometrical Journal. Accepted.
• Eliana Christou, Eftychia Solea, Shanshan Wang, and Jun Song* (2027)

  Sufficient Dimension Reduction for the Conditional Quantiles of Functional Data

  Statistica Sinica. Accepted. [Link]
• SeungBeom Hong^†, Ilmun Kim, and Jun Song* (2024)

  Enhancing Sufficient Dimension Reduction via Hellinger Correlation

  Proocedings of the 41st International Conference on Machine Learning, PMLR 235:18634-18647. (ICML 2024). [Link][arXiv] [Code]
• Eftychia Solea, Eliana Christou, and Jun Song* (2026)

  Robust Inverse Regression for Multivariate Elliptical Functional Data

  Statistica Sinica. Accepted. [Link]
• Sehun Jang^† and Jun Song* (2024)

  A selective review of nonlinear sufficient dimension reduction

  Communications for Statistical Applications and Methods. 31(2): 247-262. [Link]
• Jun Song, Kyongwon Kim, and Jae Keun Yoo (2023)

  On a Nonlinear Extension of the Principal Fitted Component Model

  Computational Statistics and Data Analysis. 182, 107707. [Link]
• Chuqin Li, Alex Jordan, Jun Song, Yaorong Ge, and Albert Park (2022)

  A Novel Approach to Characterize State-level Food Environment and Predict Obesity Rate Using Social Media Data: Correlational Study

  Journal of Mdeical Internet Research. 24(12): e39340. [Link]
• Ali Mahzarnia^† and Jun Song* (2022)

  Multivariate functional group LASSO: functional predictor selection

  PLoS ONE. 17(4): e0265940. [Link] [arXiv] [Code]
• Bing Li and Jun Song (2022)

  Dimension reduction for functional data based on weak conditional moments

  The Annals of Statistics. 50(1): 107-128. [Link]
• Jun Song and Kyongwon Kim (2022)

  Sparse Multivariate Functional Principal Component Analysis

  Stat. 11: e435. [Link]
• Paul H. Jung^† and Jun Song* (2022)

  Multivariate Neighborhood Trajectory Analysis: An Exploration of the Functional Data Analysis Approach

  Geographical Analysis. 54(4): 789-819. [Link]
• Jun Song* and Bing Li (2021)

  Nonlinear and additive principal component analysis for functional data

  Journal of Multivariate Analysis. 181: 104675. [Link] [Code]
• Jun Song* (2019)

  On sufficient dimension reduction for functional data: Inverse moments based methods

  Wiley Interdisciplinary Reviews: Computational Statistics. 11(4): e1459. [Link]
• Holly Holt, Gabriel Villar, Weiyi Cheng, Jun Song, and Christina Grozinger (2018)

  Molecular, physiological and behavioral responses of honey bee (Apis mellifera) drones to infection with microsporidian parasites

  Journal of Invertebrate Pathology. 155: 14-24. [Link]
• Bing Li and Jun Song (2017)

  Nonlinear sufficient dimension reduction for functional data

  The Annals of Statistics. 45(3): 1059-1095.[Link] [Code]

Submitted

• Sehun Jang^† and Jun Song*

  Interaction-Aware Nonlinear Scalar-on-Function Regression with Functional Predictor Selection via Adaptive RKHS

  submitted.

Instructor at

• DAS 512: Principles of Statistical Learning II
Summer 2024, Fall 2025.
• DAS 511: Principles of Statistical Learning I
Spring 2024, Summer 2025.
• STA 829: Topics in Applied Statistics I (Functional Data Analysis)
Fall 2023.
• STA 617: Advanced Statistical Machine Learning
Fall 2021, Spring 2025.
• STA 518: Statistical Methodology for Data Analysis
Fall 2022.
• STAT 433: Statistical Modeling for Deep Learning
Spring 2022.
• STAT 424: Statistical Machine Learning
Fall 2023, Fall 2025.
• STAT 232: Mathematical Statistics
Fall 2021.
• STAT 221: Introduction to Probability Theory
Spring 2022, Fall 2022, Spring 2023, Spring 2024, Spring 2025.
• Workshop (8 hours), R을 이용한 기초 통계학
Winter 2021, Summer 2022.
• Workshop (8 hours), 로지스틱 회귀분석
Summer 2022.
• Coding Bootcamp (10 hours), R & Python for Data Science
Winter 2022, Winter 2023.

• STAT 7133/8133: Multivariate Analysis
Spring 2018, Spring 2019.
• STAT 6115/DSBA 6115: Statistical Learning with Big Data
Fall 2018, Fall 2019, Fall 2020, Fall 2021.
• STAT 3122: Probability and Statistics I
Spring 2019.
• STAT 2122: Introduction to Probability and Statistics
Spring 2020, Fall 2020, Spring 2021, Fall 2021.
• STAT 1221/1222: Elements of Statistics I / Introduction to Statistics
Fall 2017, Fall 2018, Fall 2019, Spring 2020.

• STAT 414/MATH 414: Introduction to Probability Theory
Summer 2016.
• STAT 318/MATH 318: Elementary Probability
Fall 2015 and Spring 2016.
• STAT 414: Introduction to Probability Theory
Summer 2015.

Current Students

Former Students

• 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
• Yujin Hwang (황유진) (M.S. in August 2024), Pennsylvania State University (Statistics Ph.D. Student)
Thesis: Nonparametric Variable Selection for Mixed Model
• Dong Hyun Kang (강동현) (M.S. in August 2024), Financial Supervisory Service (금융감독원)
Thesis: Synthetic data generation via distorted principal loadings: Application to data integration
• Dongcheol Shin (신동철) (M.S. in August 2024), PwC Consulting
Thesis: A Unified Framework for Synthetic Data Generation of Tabular Data via Self-Attention
• 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), Aivelabs
Thesis: Penalized neural network sufficient dimension reduction

• 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)