Principles, Statistical and Computational Tools for Reproducible Data Science

Dr. Christine Choirat is a research associate at the Institute for Quantitative Social Science (IQSS) and research scientist in the Biostatistics Department at the Harvard T.H. Chan School of Public Health. Christine was trained as a statistician and has over twenty journal and conference publications. Her research focuses on quantitative methods applied to economics, management, decision theory and psychology, with a special emphasis on experimental approaches. She is particularly interested in issues related with governance and ethics.

Dr. Lorenzo Trippa is an associate professor of Biostatistics in the Biostatistics Department at the Harvard T.H. Chan School of Public Health. He is interested in the development of adaptive clinical trial designs, and his research includes the study of algorithms and methodologies for the analysis of data generated by adaptive trials. He is also particularly interested in Bayesian nonparametrics, a great source of modeling opportunity in biomedical applications as well as both computational and theoretical problems.

Dr. John Quakenbush is a professor of Computational Biology and Bioinformatics in the Biostatistics Department at the Harvard T.H. Chan School of Public Health and Dana-Farber Cancer Institute, as well as the director of the Center for Cancer Computational Biology (CCCB). His research group focuses on methods spanning the laboratory to the laptop that are designed to use genomic and computational approaches to reveal the underlying biology. In particular, they have been looking at patterns of gene expression in cancer with the goal of elucidating the networks and pathways that are fundamental in the development and progression of the disease.

Dr. Curtis Huttenhower is an associate professor of Computational Biology and Bioinformatics in the Biostatistics Department at the Harvard T.H. Chan School of Public Health and director of the Huttenhower Lab. His research focuses on understanding the function of microbial communities, particularly that of the human microbiome in health and disease. This work entails a combination of computational methods development for wrangling large data collections, as well as biological analyses and laboratory experiments to link the microbiome in human populations to specific microbiological mechanisms.