Please see the homepage for a brief explanation of current research projects.

Research Interests

My research primarily focuses on Gaussian processes (GPs), a Bayesian non-parametric based modeling method, which I have utilized across various disciplines. These include:

A significant aspect of my research is to bridge well-founded methodologies in probability and statistics with modern data science challenges. Unlike the black box nature of neural networks, Gaussian processes, through their kernel structure, provide insights into the properties of the function being modeled, such as differentiability, continuity, and periodicity. This approach underlines my commitment to developing transparent, interpretable, and robust solutions to state-of-the-art problems in data science.



Books and Book Chapters