My research focuses on Data-Centric and Reliable Machine Learning, with particular emphasis on learning from imbalanced, complex, and imperfect datasets. I investigate advanced resampling and data-generation methods, synthetic data generation, data quality and dataset characterization, anomaly detection, and machine-learning evaluation. A central objective of my research is to develop methods that improve the generalizability, robustness, and reliability of machine-learning models in real-world settings. My emerging research interests include generative and agentic AI for automating data-centric machine-learning workflows, including dataset diagnosis, synthetic data generation, and model development.