Alind Gupta is statistician in the Real-world and Advanced Analytics team at Cytel focusing on machine learning and probabilistic modeling. His prior work has focused on graphical models in areas of heart disease, diabetes, chronic infectious disease and cancer for informing clinical risk prediction and health economic models. These have involved working with data from randomized trials, observational datasets from cancer registries and national health surveys, and unstructured biomedical text. Alind has a PhD from University of Toronto studying rare diseases.

Miguel Hernán conducts research to learn what works for the treatment and prevention of cancer, cardiovascular disease, and HIV infection. Together with his collaborators, he designs analyses of healthcare databases, epidemiologic studies, and randomized trials. Miguel teaches clinical data science at the Harvard Medical School, clinical epidemiology at the Harvard-MIT Division of Health Sciences and Technology, and causal inference methodology at the Harvard T.H. Chan School of Public Health, where he is the Kolokotrones Professor of Biostatistics and Epidemiology. His edX course Causal Diagrams and his book Causal Inference, co authored with James Robins, are freely available online and widely used for the training of researchers.