Research Interest

My research interest resides in developing cutting-edge Bayesian methodologies to address high dimensional classification and optimization problems related to spatio-temporal data. Specifically, my focus is developing feature extraction and estimation methods, while accounting for the cross-sectional correlations frequently observed in spatio-temporal data. This involves exploring sparsity inducing prior distributions, investigating techniques to incorporate the spatio-temporal structure into the model.

My research seeks to provide theoretical understanding and practical tools for efficient modeling and inference in complex settings with novel application. I am intrigued with examining neuroimaging data to unravel functional connectivity patterns between distinct brain regions, contributing to the broader discourse on cognitive and behavioral states. My motivation lies in the critical public health concerns surrounding mental-related illnesses which can be address through neuroimaging analysis. Leveraging Bayesian feature extraction and estimation procedures, I am driven to develop accurate subject-level prediction approaches to predict early onset of mental-related illnesses.

In conclusion, with interests in Bayesian methodologies, high dimensional spatio-temporal data analysis, and their application in neuroimaging, my research aims to be at the forefront of innovative statistical techniques with real-world implications.