Lee Cooper, PhD is an Associate Professor of Pathology in the Feinberg School of Medicine at Northwestern, and is Director of the Computational Pathology and the Center for Computational Imaging and Signal Analytics. Prior to joining Northwestern in 2019, he was an Assistant Professor of Biomedical Engineering at the Georgia Institute of Technology and Emory University. His research explores how to utilize data generated in the pathology lab to improve diagnosis and prognostication by integrating advanced molecular platforms with quantitative phenotypic measurements from digital pathology images. These activities combine fundamental machine learning research with the development of computing infrastructure, pathology informatics tools, and data resources. This work has been funded by the US National Institutes of Health NLM, NCI, NIBIB, NINDS, and NIDDK.

Dr. David Ohlssen is currently Advanced Exploratory Analytics head, within the Novartis Advanced Methodology and Data Science group, based in East Hanover New Jersey. Since joining Novartis in 2007, he has developed a broad range of experience in applying novel quantitative approaches within a drug development setting. His current focus involves driving the appropriate application of data science, machine learning and advanced modeling in a drug development setting. As part of the Novartis data digital transformation, he is heavily involved in large scale collaborations with the Oxford Big Data Institute, Carnegie Mellon University, and the Food and Drug Administration. Each of these projects examine databases that comprise of a combination of clinical, omics and imaging data, with the aim of gaining a better understanding of disease progression and a more personalized approach to treatment by using combinations of statistics, machine learning and causal inference.

Previously, after completing his PhD in Biostatistics at the University of Cambridge, he worked as a research fellow at the MRC Biostatistics Unit (Cambridge UK), where his interests included: diagnostics for Bayesian models, novel clinical trial design and statistical methods for the profiling of health-care providers. In 2016 he received the Novartis leading scientist award for his contributions to quantitative decision making in drug development and in 2021 he became a Fellow of the American Statistical Association for advancing the role of statistical and data sciences in pharmaceutical industry.

Dr. Vince D. Calhoun is founding director of the tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) and a Georgia Research Alliance eminent scholar in brain health and image analysis where he holds appointments at Georgia State University, Georgia Institute of Technology and Emory University. He was previously the President of the Mind Research Network and Distinguished Professor of Electrical and Computer Engineering at the University of New Mexico. He is the author of more than 850 full journal articles and over 850 technical reports, abstracts and conference proceedings. His work includes the development of flexible methods to analyze functional magnetic resonance imaging data such as independent component analysis (ICA), deep learning for neuroimaging, data fusion of multimodal imaging and genetics data, neuroinformatics tools, and the identification of biomarkers for disease. His research is funded by the NIH and NSF among other funding agencies. Dr. Calhoun is a fellow of the Institute of Electrical and Electronic Engineers, The American Association for the Advancement of Science, The American Institute of Biomedical and Medical Engineers, The American College of Neuropsychopharmacology, and the International Society of Magnetic Resonance in Medicine. He served as the chair for the Organization for Human Brain Mapping from 2018-2019 and is a past chair of the IEEE Machine Learning for Signal Processing Technical Committee. He currently serves on the IEEE BISP Technical Committee and is also a member of IEEE Data Science Initiative Steering Committee as well as the IEEE Brain Technical Committee.

Ryan Copping is the Global Head of Data Science Acceleration at Roche & Genentech where he leads a team of data scientists, data engineers and software developers to develop products and capabilities that enable scientific insight generation for the clinical trial portfolio. Ryan has worked for Roche for 18 years and has held multiple data science leadership roles before his current role including building and leading the personalized healthcare analytics team who generated novel insights from real world data sources including electronic medical records, omics datasets and images. Ryan also leads the Roche Advanced Analytics Network (RAAN) which is a community of over 1300 AI and machine learning enthusiasts from 40 Roche locations across the globe. Ryan’s background is in Statistics and Computing and he has a passion for advanced analytics, new technology and understanding & fostering team culture and engagement.