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Professors

Gunwoong Park Assistant professor

MajorStatistical A.I. & Machine learning, Graphical model learning, High-dimensional and robust learning, Causal inference
Office25-428
Contact 880-2624
Emailgwpark23@snu.ac.kr
RESEARCH AREAS

Statistical A.I. & Machine learning

Graphical model learning

High-dimensional and robust learning

Causal inference

DEGREES

2003 – 2011

B.S. in Statistics, Seoul National University.

2011 – 2016

Ph.D. in Statistics, University of Wisconsin – Madison.

BIOGRAPHY

2022.03 – Prsent

Assistant Professor, Department of Statistics, Seoul National University

2018.03 – 2022.02

Assistant Professor, Department of Statistics, University of Seoul

2016.09 – 2018.02

Postdoctoral Research Fellow, Department of Statistics, University of Michigan – Ann Arbor.

SELECTED PUBLICATIONS

Park, S., & Park, G. (2022). Robust estimation of Gaussian linear structural equation models with equal error variances. Journal of the Korean Statistical Society, 1-22.

Park, G., & Kim, Y. (2021). Learning high-dimensional gaussian linear structural equation models with heterogeneous error variances. Computational Statistics & Data Analysis, 154, 107084.

Park, G., Moon, S. J., Park, S., & Jeon, J. J. (2021). Learning a high-dimensional linear structural equation model via l1-regularized regression. Journal of Machine Learning Research, 22(102), 1-41.

Park, G., & Kim, Y. (2020). Identifiability of gaussian linear structural equation models with homogeneous and heterogeneous error variances. Journal of the Korean Statistical Society, 49(1), 276-292.

Park, G. (2020). Identifiability of Additive Noise Models Using Conditional Variances. Journal of Machine Learning Research, 21(75), 1-34.

Park, G., & Park, H. (2019). Identifiability of generalized hypergeometric distribution (ghd) directed acyclic graphical models. In The 22nd International Conference on Artificial Intelligence and Statistics (pp. 158-166). PMLR.

Park, G., & Park, S. (2019). High-Dimensional Poisson Structural Equation Model Learning via \ell_1-Regularized Regression. Journal of Machine Learning Research, 20, 95-1.

Park, G., & Raskutti, G. (2017). Learning Quadratic Variance Function (QVF) DAG Models via OverDispersion Scoring (ODS). Journal of Machine Learning Research, 18, 224-1.

Park, G., & Raskutti, G. (2015). Learning large-scale poisson dag models based on overdispersion scoring. Advances in neural information processing systems, 28.

AWARDS

2021

한국통계학회 “2021년 SAS 신진통계학자 학술논문상”

2021

한국과학기술단체총연합회 “2021년 과학기술우수논문상”

2016

Institute of Mathematical and Statistics (IMS) Travel Award