Hi, this is Fahad, I am glad you have made it to my page. I am a Biostatistician, working as an assistant professor of biostatistics and biomedical data science at Arizona State University. I completed an M.S. and Ph.D. in Statistics from TEXAS TECH in 2022 and 2024, respectively. Before moving to the US as a graduate student at TEXAS TECH, I obtained my B.S. in Mathematics and M.S. in Applied Mathematics from Dhaka University. I worked as a Postdoctoral research scientist in Biostatistics from University of Colorado School of Medicine of CU Anschutz and also worked as an ORISE fellow in Machine Learning at NCTR, FDA. Also I worked as a Biostatistical consultant at TTUHSC and the NEM Research Institute of Rutgers New Jersey Medical School.

My research interest evolves in many areas within Statistics, Machine Learning/AI model development and implementation, Biomedical Data Science, Stochastic Epidemiology, Computational Biology, and Biostatistics. Inspired by Debabrata Basu's groundbreaking contributions to statistics, I delved deeper into the field, immersing myself in his methodologies and insights. I pursued advanced studies in statistics, ultimately becoming a dedicated statistician committed to pushing the boundaries of knowledge in the discipline.

My interest in statistics includes machine learning (ML), data science and statistical uncertainty quantification (UQ). My recent work focuses on the detection and interpretation of the patterns of usually high-dimensional biomedical data with complex structures, particularly when applied to cancer infrared tomography, cancer genomics, chemical structure, EHR data, population and epidemiology data, and medical images. In my interdisciplinary research, I combine statistical machine learning and biomedical data science to advance human disease diagnosis and monitoring, with a particular focus on high-dimensional data-driven model developments in cancer research. Moreover, my current work encompasses various aspects of statistical modeling, UQ, and ML techniques to improve the accuracy, efficiency and reliability of disease prediction, diagnosis, treatment monitoring, and drug design.