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-focused statistical methods
- Computational statistics
- Causal inference
- Optimal transportation
Teaching
Classes (Semester 1/2024)
229351 - Statistical Learning for Data Science 1208891 - Special Topics: Causal Inference
Prior classes
229352 - Statistical Learning for Data Science 2208711 - Statistical Theory 1
208424 - Optimization for Statistical Learning
208772 - Statistical and Machine Learning
208780 - Linear Statistical Models
208891 - Special Topics: 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.