Ph.D. Students

MS Students

Undergraduates (Lab Intern/Individual Research/Undergraduate Research Projects)

Nikolai Kurlovich (KAIST Math, 2022-2023)

Diyar Tulenov (KAIST CS, 2022-2023, URP)

조성빈 (KAIST Math/ISE, 2023)

황규림 (KAIST Math, 2023, URP)

최지호 (KAIST Math, 2023)

이대환 (KAIST ISE, 2023)

윤대한 (KAIST Math, 2023)

심우용 (KAIST ISE, 2023-2024, URP)

이정준 (KAIST ISE, 2023)

홍진영 (KAIST ISE, 2024)

안정원 (KAIST ISE, 2024)

박규태 (KAIST ISE, 2024)

안병준 (KAIST ISE, 2025)

송진형 (KAIST ISE, 2025)

이찬민 (KAIST ISE, 2025)

Former Students

• Hyunbin Choi (MS. 2025): "Dynamic Sample Weighting for Classification with Asymmetric Label Noise: Application to Postoperative Complication Prediction"

• Jiyu Moon (MS. 2025): "Predicting Sleep-Wake Times from Wearable Sensor Data using Gaussian Mixtures", KT, South Korea

• Daeyoung Kim (MS. 2024): "Graph-level Outlier Detection with Normalized Graph Kernel", SK hynix, South Korea

• Hyunwoo Cha (MS. 2024): "Ovarian Cancer Risk Prediction and Decision Making for Screening in Clinical Practice", 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", Hyundai Movex, South Korea

• Seongbin An (MS. 2024): "Monotone Clustering with Sparse Generalized Additive Model", 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, Assistant Professor, Major of Big Data Application, Sungkonghoe 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