Jun Song

I am an Assistant Professor in the Department of Mathematics and Statistics and an Affiliated Faculty in the School of Data Science at The University of North Carolina at Charlotte. My research interests include dimension reduction, functional data analysis, statistical machine learning, and graphical models. Prior to working at UNC Charlotte, 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.

Download my CV.

Last modified May, 2020.

Education

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Academic Position

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Grants & Awards

• 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 problem
• Statistical analysis of complex data structure, including functional data and tensor data
• Statistical machine learning

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Publications

* indicates a UNC Charlotte student.

• Bing Li and Jun Song (2020+)

  Dimension reduction for functional data based on weak conditional moments

  submitted to The Annals of Statistics (under major revision)
• Paul H. Jung* and Jun Song(2020+)

  Multivariate Neighborhood Trajectory Analysis: A Critique and Propose of Functional Data Analysis Approach

  submitted to Geographical Analysis (under major revision)

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• Jun Song and Bing Li (2020+)

  Nonlinear and additive principal component analysis for functional data

  Journal of Multivariate Analysis (in press)
• Jun Song (2019)

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

  WIREs: Computational Statistics, 11(4), e1459.
• 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.
• Bing Li and Jun Song (2017)

  Nonlinear sufficient dimension reduction for functional data

  The Annals of Statistics, 45, 1059--1095.

In preparation

• Ali Mahzarnia* and Jun Song

  Functional covariate selection for multivariate functional data

• Bing Li and Jun Song

  Sufficient dimension reduction for general tensor product

• Jun Song, Kyongwon Kim, and Bing Li

  Sparse Multivariate Functional PCA

• Jun Song and Won Chang

  Calibration of High-dimensional Spatial Data via nonlinear sufficient dimension reduction

• Jun Song, Naomi S. Altman, and Kalyan Das

  Self-modeling Nonlinear Poisson regression model

• Jun Song, Bing Li, and Hannu Oja

  On functional Spearman's correlation and the related canonical correlation analysis

Instructor at

• STAT 7133/8133: Multivariate Analysis (graduate course)
Spring 2018, Spring 2019.
• STAT 6115/DSBA 6115: Statistical Learning with Big Data (graduate course)
Fall 2018, Fall 2019, Fall 2020.
• STAT 3122: Probability and Statistics I
Spring 2019.
• STAT 2122: Introduction to Probability and Statistics
Spring 2020, Fall 2020.
• 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 (Online)
Summer 2015.