The Great Plains IDeA-CTR BERD Core Presents:
Precision Medicine Workshop
The 2020 Precision Medicine Workshop, presented by the BERD Core of the Great Plains IDeA-CTR, featured two globally recognized experts to present their research with the aim to translate precision medicine into improvements in health care across the clinical and translational spectrum. This workshop included an AM and a PM session, focusing on different aspects of precision medicine and emerging technology. This highly attended workshop included more than 240 registered attendees from across the country.
Morning Session: Artificial Intelligence, Machine Learning, and Precision Medicine
Dr. Fu is a Fellow of the American Statistical Association. He is also an adjunct professor of biostatistics at the Indiana University School of Medicine. Dr. Fu received his PhD in statistics from the University of Wisconsin-Madison in 2007 and joined Eli Lilly after that. Since then, Dr. Fu is very active in statistics methodology research. He has published more than 90 articles in peer-reviewed journals in multiple areas, including Bayesian adaptive design, survival analysis, recurrent event modeling, personalized medicine, indirect and mixed treatment comparison, joint modeling, Bayesian decision making, and rare events analysis.
In recent years, his research has focused on machine learning and artificial intelligence. His research has been published in various top journals including JASA, JRSS, Biometrics, ACM, IEEE, JAMA, Annals of Internal Medicine,etc. He regularly teaches topics of machine learning and AI in large industry conferences, including a recent FDA workshop. He has served on the board of directors for statistics organizations, including program chairs and committee chairs for the ICSA, ENAR, and ASA Biopharmaceutical session.
This half-day short course provides an overview of statistical machine learning, and artificial intelligence techniques with applications to the precision medicine, in particular, to deriving optimal individualized treatment strategies for personalized medicine.
Afternoon Session: Applications of Deep Learning and Inverse-Reinforcement Learning to Precision Medicine
Professor Kosorok received his PhD in Biostatistics from the University of Washington in 1991. He is an internationally known biostatistician and a prominent expert in data science, machine learning and precision medicine. He is a fellow of American Statistical Association, Institute of Mathematical Statistics, and American Association for the Advancement of Sciences. He has published more than 160 peer-reviewed journal articles with more than 50 appeared in the premier statistical journals such as Annals of Statistics, JASA, JRSS-B, Biometrikaand Biometrics. He has also written a major text on theoretical foundations in empirical processes and semiparametric inferences(Kosorok, 2008, Springer) as well as co-edited (with Erica E.M. Moodie, 2016, ASA-SIAM) a research monograph on dynamic treatment regimens and precision medicine.
As principal investigator, he has constantly received major research grants from NIH and NSF. Currently, he leads P01 CA 142538-Statistical Methods for Cancer Clinical Trials and is the Director of Biostatistics core and Co-project leader for North California Translational & Clinical Sciences Institute. In addition, he is a distinguished educator in Statistics/Biostatistics graduating 46 PhD students.
This half-day short course provides an overview of Deep Learning (DL), Inverse-Reinforcement Learning (IRL), and Machine Learning (ML) techniques as they relate to Precision Medicine applications.