My research interest is in predictive modeling and machine learning.
I am a doctoral student in the department of Statistics. I have a background in Mathematical sciences, Applied statistics, and Biostatistics. My interest is to explore and apply innovative statistical methods to data analysis, including clinical, time-to-event, and epidemiological data. My current research aims at improving the quality of care and prediction of stroke outcome using modern machine learning algorithms. I work with both simulated and real () datasets.
I have been part of the teaching team for various courses, including Business Analytics (undergraduate), Statistics for Business and Economics (undergraduate/graduate), Quantitative Research Methods for the Social Sciences (graduate/postgraduate), and Statistics in Medicine (undergraduate). Additionally, from 2017 to 2019, I served as a tutorial fellow for mathematics and statistics courses at .