Miguel Hernán, MD, ScM, DrPH is Professor, Departments of Epidemiology and Biostatistics, Harvard School of Public Health, and Affiliated Faculty member of the Harvard-MIT Division of Health Sciences and Technology. His research and teaching focus on methods for causal inference, including comparative effectiveness of policy and clinical interventions. He is Associate Director of the HSPH Program on Causal Inference, Editor of Epidemiology, Associate Editor of the American Journal of Epidemiology and of the Journal of the American Statistical Association, former Associate Editor of Biometrics, and elected Fellow of the American Association for the Advancement of Science. He has served on committees for the U.S. Institute of Medicine and the National Research Council.
Finale Doshi-Velez is an assistant professor in Computer Science at the Harvard Paulson School of Engineering and Applied Sciences. She completed her MSc from the University of Cambridge as a Marshall Scholar, her PhD from MIT, and her postdoc at Harvard Medical School. Her interests lie at the intersection of machine learning, healthcare, and interpretability.
Barbara Engelhardt, an assistant professor, joined Princeton in 2014 from Duke University where she had been an assistant professor in Biostatistics and Bioinformatics and Statistical Sciences. She graduated from Stanford University and received her Ph.D. from the University of California, Berkeley, advised by Professor Michael Jordan. She did postdoctoral research at the University of Chicago, working with Professor Matthew Stephens, and three years at Duke University as an assistant professor. Interspersed among her academic experiences, she spent two years working at the Jet Propulsion Laboratory, a summer at Google Research, and a year at 23andMe, a DNA ancestry service. Professor Engelhardt received an NSF Graduate Research Fellowship, the Google Anita Borg Memorial Scholarship, and the Walter M. Fitch Prize from the Society for Molecular Biology and Evolution. She also received the NIH NHGRI K99/R00 Pathway to Independence Award. Professor Engelhardt is currently a PI on the Genotype-Tissue Expression (GTEx) Consortium. Her research interests involve statistical models and methods for analysis of high-dimensional data, with a goal of understanding the underlying biological mechanisms of complex phenotypes and human diseases.
Katherine is an Assistant Professor at Duke University, in the Department of Statistical Science and at the Center for Cognitive Neuroscience. Prior to joining Duke she was an NSF Postdoctoral Fellow, in the Computational Cognitive Science group at MIT, and an EPSRC Postdoctoral Fellow at the University of Cambridge. Her Ph.D. is from the Gatsby Unit, where her advisor was Zoubin Ghahramani.
Katherine's research interests lie in the fields of machine learning and Bayesian statistics. Specifically, she develops new methods and models to discover latent structure in data, including cluster structure, using Bayesian nonparametrics, hierarchical Bayes, techniques for Bayesian model comparison, and other Bayesian statistical methods.
Paul Varghese, MD MMSc is Head of Health Informatics for Verily (Google Life Sciences). A cardiologist and clinical informatician, Dr. Varghese helps lead Verily's efforts in the application of advanced data science methodologies to improve patient outcomes and patient-provider interactions. Prior to joining Verily, Dr. Varghese served as Medical Director for Cardiovascular IT at Agfa Healthcare, responsible for product design, interoperability, and quality/regulatory affairs. He currently serves on the following American College of Cardiology efforts: the Echocardiography Data Standards Task Force; the HIT Task Force; and the Innovation Advisory Group.
Dr. Varghese holds an Sc.B. and M.D. from Brown University, and a MMSc in biomedical informatics from Harvard Medical School. He did his internal medicine residency training on the Osler Medical Service at Johns Hopkins Hospital; cardiology fellowship at UCSF, where he specialized in heart failure and echocardiography; and quality improvement / patient safety training at Intermountain Healthcare's Advanced Training Program.
Saria’s interests span machine learning, computational statistics, and its applications to domains where one has to draw inferences from observing a complex, real-world system evolve over time. The emphasis of her research is on Bayesian and probabilistic graphical modeling approaches for addressing challenges associated with modeling and prediction in real-world temporal systems. In the last seven years, she has been particularly drawn to computational solutions for problems in health informatics.
Prior to joining Johns Hopkins, she earned her PhD and Masters at Stanford in Computer Science working with Dr. Daphne Koller. She also spent a year at Harvard University collaborating with Dr. Ken Mandl and Dr. Zak Kohane as an NSF Computing Innovation Fellow. While in the valley, she also spent time as an early employee at Aster Data Systems, a big data startup acquired by Teradata. She enjoys consulting and advising data-related startups. She is an investor and an informal advisor to “Patient Ping”.