Students
Ph.D. Students
Changwon Yoon
Department of Industrial and Systems Engineering
Research Interest: non-Euclidean data analysis, Data Privacy
E-mail: cwyoon@kaist.ac.kr
Giwon Kim
Graduate School of Data Science
Research Interest : Statistical Machine Learning
E-mail : 71_won@kaist.ac.kr
Yian Kim
Graduate School of Data Science
Research Interest : Statistical Learning
E-mail : yian9995@kaist.ac.kr
Bonwoo Lee
Department of Mathematical Science (Co-advising)
MS Students
Jiyu Moon
Graduate School of Data Science
Research Interest : Machine Learning
E-mail : jy_moon@kaist.ac.kr
Hyunbin Choi
Graduate School of Data Science
Research Interest : Machine Learning
E-mail : hyunbin98@kaist.ac.kr
Undergraduates (Lab Intern/Individual Research/Undergraduate Research Projects)
Nikolai Kurlovich (KAIST Math, 2022-2023)
Diyar Tulenov (KAIST CS, 2022-2023)
조성빈 (KAIST Math/ISE, 2023)
황규림 (KAIST Math, 2023)
최지호 (KAIST Math, 2023)
이대환 (KAIST ISE, 2023)
윤대한 (KAIST Math, 2023)
심우용 (KAIST ISE, 2023-2024)
이정준 (KAIST ISE, 2023)
홍진영 (KAIST ISE, 2024)
안정원 (KAIST ISE, 2024)
박규태 (KAIST ISE, 2024)
Former Students
Daeyoung Kim (MS. 2024): "Graph-level Outlier Detection with Normalized Graph Kernel"
Hyunwoo Cha (MS. 2024): "Ovarian Cancer Risk Prediction and Decision Making for Screening in Clinical Practice", Data Scientist, Samsung Electronics, South Korea
Junyoung Park (Ph.D., 2024): "Kernel Methods for Compositional Data and Dimension Reduction", Post-doc at University of Michigan, Ann Arbor, USA
Yunji Kim (MS. 2024): "Multiple imputation using predictive mean matching for canonical correlation analysis with block-wise missing data"
Seongbin An (MS. 2024): "Monotone Clustering with Sparse Generalized Additive Model", Data Scientist, Samsung Electronics, South Korea
Binglin Li (Ph.D., 2023): "Geometric Techniques in Data Science", Assistant Professor, North Carolina Agricultural and Technical State University, USA
Di Xiao (Ph.D. 2021): "Learning Structure Changes in High-dimensional and Heavy-tailed Time Series", 2022, AI and Data Science Associate, JP Morgan, USA
Seungwoo Kwak (Ph.D. 2021): “Application of differential privacy to a goodness-of-fit test and data release problems”, 2021, Post-doc at Seoul National University, South Korea
Ziyang Ma (Ph.D. 2021): “New Regularization Methods for Supervised Learning with High-Dimensional Data”, 2021, Quantitative Analytics Specialist, Wells Fargo Bank, USA
JC Poythress (Ph.D. 2020): “Regularization techniques for statistical methods utilizing matrix/tensor decompositions”, 2020, Assistant Professor, Department of Mathematics and Statistics, University of New Hampshire, USA
Hee Cheol Chung (Ph.D. 2020): “Some Contributions to Statistical Inference on Small Sample Size Data: Small Area Estimation and High Dimension Low Sample Size Data Analysis”, 2020, Assistant Professor, Department of Mathematics and Statistics, University of North Carolina-Charlotte, USA
Debin Qiu (Ph.D. 2015): “Grouped Variable Screening for Ultrahigh Dimensional Data under Linear Model”, 2015, Vice President, Applied AI/ML Lead at JPMorgan Chase & Co, USA
Soyeon Jung (MS. 2015): “A New Approach for Error Correction in Multiple Goodness-of-Fit tests”, 2015, Senior Statistician, T-Mobile, USA
Sandra S. Safo (Ph.D. 2014): “Design And Analysis Issues In High Dimension, Low Sample Size Problems”, 2014, Assistant Professor, Division of Biostatistics, University of Minnesota, USA
Jung Ae Lee-Bartlett (Ph.D. 2013): “Sample Integrity in High Dimension“, 2013, Assistant Professor, University of Messachusetts Medical School, USA