Dwaipayan Mukhopadhyay, Ph.D., M.S., is an assistant professor of epidemiology and biostatistics in the School of Public Health at the University of Nevada, Las Vegas. He is a biostatistician and statistician whose work focuses on statistical methodology, machine learning, and data science, with applications to biomedical, clinical, population health, and other data-intensive scientific questions. He has a strong interest in collaborative research and welcomes opportunities to work with investigators across a wide range of disciplines where rigorous quantitative methods can contribute to scientific discovery.

His methodological interests include machine and statistical learning, statistical modeling and inference, survival and time-to-event analysis, predictive modeling, high-dimensional and longitudinal data analysis, model calibration and reliability, reproducibility, generalizability, and transportability. He is particularly interested in quantitative problems that require developing or adapting statistical and computational methods for new scientific questions, complex data structures, and diverse populations. His work draws on a range of data sources, including electronic health records, real-world clinical, registry, and population-based data, survey data, genomic, transcriptomic, and other high-dimensional molecular data, as well as emerging digital health, wearable, and multimodal data.

His applied research spans cancer, clinical research, population health, behavioral health, and other areas involving longitudinal outcomes and complex prediction problems. He is broadly interested in investigating new scientific questions and extending statistical and data-science methods into new substantive areas. He values learning from domain experts and approaching unfamiliar problems with methodological rigor, curiosity, and openness to new quantitative approaches. He views biostatistics as an inherently collaborative discipline and values partnerships in which statisticians and subject-matter experts work together from study design and methodological development through analysis, interpretation, and dissemination.