Reproducible Data Science: Tools and Principles

Curtis Huttenhower, Associate Professor of Computational Biology and Bioinformatics at Harvard T.H. Chan School of Public Health, has revolutionized our understanding of microbial communities and their impact on human health. After earning his Ph.D. in Genomics and Computational Biology from Princeton University, he has built an extraordinary career combining computational innovation with biological discovery. As director of the Huttenhower Lab and co-director of the Harvard Chan Microbiome in Public Health Center, he leads groundbreaking research on the human microbiome's role in health and disease. His work includes leadership roles in the NIH Human Microbiome Project and development of widely-used computational tools for microbiome analysis. His research has produced over 200 peer-reviewed publications, earning him recognition as a Highly Cited Researcher by Clarivate Analytics and the Presidential Early Career Award for Scientists and Engineers (PECASE). Through his innovative approaches combining machine learning, statistical methods, and biological experimentation, he continues to advance understanding of how microbial communities influence human health while mentoring the next generation of computational biologists.

John Quackenbush, Professor of Computational Biology and Bioinformatics at Harvard T.H. Chan School of Public Health and Chair of the Department of Biostatistics, has transformed our understanding of genomics and cancer biology through innovative data analysis approaches. After earning his PhD in Theoretical Physics, he made a pivotal career shift in 1992 when he received a fellowship to work on the Human Genome Project, leading him through positions at the Salk Institute, Stanford, and The Institute for Genomic Research before joining Harvard in 2005. As director of the Center for Cancer Computational Biology at Dana-Farber Cancer Institute, he pioneers the use of massive datasets to understand how multiple small effects combine to influence human health and disease. His impressive research portfolio includes over 300 scientific papers with more than 73,000 citations, while his work spans from developing new analytical methods for microarray analysis to creating sophisticated network models of cancer biology. His achievements have earned him recognition as a White House Open Science Champion of Change in 2013, and his leadership extends to serving on multiple scientific advisory boards and directing major research initiatives in cancer genomics and precision medicine.

Lorenzo Trippa, Professor in the Department of Biostatistics at Harvard T.H. Chan School of Public Health and Dana-Farber Cancer Institute, has established himself as a pioneering figure in clinical trial design and Bayesian statistics. After earning his Ph.D. in Statistics from Bocconi University and MS in Statistics (cum laude) from Milan's Bicocca University, he has built an influential career combining theoretical statistics with practical clinical applications. His groundbreaking research focuses on adaptive clinical trial designs, Bayesian nonparametrics, and the development of prediction models in personalized medicine. As winner of the Savage Ph.D. Thesis Award from the International Society for Bayesian Analysis, his work has significantly impacted how clinical trials are designed and analyzed. His expertise spans computational methods for Bayesian inference, meta-analyses in personalized medicine, and innovative approaches to cancer clinical trials. Through his joint appointment at Dana-Farber Cancer Institute, he continues to advance statistical methodologies that improve clinical trial efficiency and patient care while mentoring the next generation of biostatisticians.

Christine Choirat, Chief Innovation Officer of the Swiss Data Science Center and former Senior Research Scientist at Harvard T.H. Chan School of Public Health, has established herself as a pioneering figure in environmental health data science. After earning her Ph.D. in Statistics from Paris Dauphine University in 2003, she has built an influential career combining statistical methodology with environmental and health policy applications. Her groundbreaking research includes major studies on air pollution's health impacts, published in prestigious journals like the New England Journal of Medicine and JAMA. As a key contributor to Harvard's environmental health research initiatives, she has developed innovative statistical methods for analyzing large-scale environmental data and their effects on public health. Her work spans high-performance computing, reproducible research, and advanced statistical modeling, with particular focus on air pollution exposure assessment and health outcomes in the Medicare population. Through her roles at Harvard's Institute for Quantitative Social Science and the Swiss Data Science Center, she continues to advance the application of data science to critical environmental and public health challenges