Research

My research lies at the intersection of machine learning, data science, and data-driven decision-making, with a focus on developing interpretable, robust, and privacy-preserving methods for analyzing high-dimensional and multimodal data. A central theme of my work is transforming diverse and irregular data streams into trustworthy insights that support organizational and societal decision-making. I develop tensor-based and federated frameworks for personalized and privacy-preserving modeling, as well as multimodal data fusion techniques for human-centered AI systems. More broadly, I aim to design human-centered and trustworthy analytics that bridge methodological rigor with real-world impact across healthcare, transportation, energy, and immersive technologies.