Dynamic prediction, deep learning, high-dimensional statistics, and precision medicine are some of the research topics that I am particularly interested in the field of. I am now conducting research with the main goal of developing statistical methods and theoretical frameworks for the purpose of predicting heterogeneous data across a wide range of samples and characteristics. 

I established a sample-specific tuning parameter calibration process for the extremely diverse classification of immunofluorescence images. This procedure was developed in close collaboration with pathologists from many universities, including Stanford, University of Louisville, and Washington University. Furthermore, in order to work on personalized genetic data prediction, I make use of high-dimensional generalized linear models. I am currently working in the Department of Bioinformatics and Biostatistics within the School of Public Health and Informatic Sciences at the University of Louisville. My primary areas of concentration include personalized survival analysis and personalized variable selection.