I received a PhD in mathematics at UCSD, where I was fortunate to be supervised by Ioan Bejenaru. My research focuses on finite-sample properties of statistical models.
Research topics
- Privacy-preserving statistical learning
- Probabilistic models / Causal inference
- Statistical learning of optimal transportation
Teaching
Classes (Semester 1/2024)
208711 - Statistical Theory 1229352 - Statistical Learning for Data Science 2
Prior classes
229351 - Statistical Learning for Data Science 1208772 - Statistical and Machine Learning
208424 - Optimization for Statistical Learning
208891 - Probabilistic Graphical Models
Publications
Preprint
Investigating Privacy Leakage in Dimensionality Reduction Methods via Reconstruction Attackwith Chayadon Lumbut
arXiv, 2024.
Journal papers
Uniform Confidence Bands for Optimal Transport Map on One-Dimensional Datawith Ryo Okano and Masaaki Imaizumi
Electronic Journal of Statistics, 2024.
Universal Consistency of Wasserstein k-NN Classifier: a Negative and Some Positive Results
Information and Inference, 2023.
Dirichlet Mechanism for Differentially Private KL Divergence Minimization (code)
Transactions on Machine Learning Research, 2023.
Detecting Anomalous LAN Activities under Differential Privacy
Security and Communication Networks, 2022.
Short-term daily precipitation forecasting with seasonally-integrated autoencoder (code)
Applied Soft Computing, 2021.
Classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma based on multi-phase CT scans
Medical Biol. Eng. Comput., 2020.
Small data well-posedness for derivative nonlinear Schrödinger equations
Journal of Differential Equations, 2018.
Conference papers
Detecting Anomalous LAN Activities under Differential Privacy.NDSS, 2022.