[Breakfast Talk] Machine Learning for Interception Medicine: Moving from Disease Treatment to Prevention and Detection

​Professor Christoph Lippert, Chair of Digital Health and Machine Learning at the Hasso Plattner Institute (HPI), will explore the transformative role of machine learning in digital health. Using case studies from his team at HPI in Potsdam, this talk will spotlight:

​- The transition from a primarily treatment-focused model to a preventative and proactive intervention approach.

​- How machine learning plays a pivotal role in uncovering the genetic intricacies behind various diseases.

​- The importance of gauging genetic risk, the potential for risk-stratified screening, and the pressing need for early medical intervention. 

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​As Chair of Digital Health and Machine Learning at the Hasso Plattner Institute, Christoph Lippert, PhD, is exploring the theory of machine learning and artificial intelligence, as well as novel applications in medicine and genomics. He focuses on advancing capabilities to predict personal health risks and supporting the personalized prevention of health issues and diseases by analyzing data from medical health records, imaging, and sequencing.

​Dr. Lippert obtained his PhD in Bioinformatics from the University of Tübingen for his work on mixed models for genome-wide association studies at the Max Planck Institute for Intelligent Systems. Prior to joining the Hasso Plattner Institute, he led a research group in Statistical Genomics at the Max Delbrück Center for Molecular Medicine in Berlin. He was also on the Data Science team at Human Longevity, Inc., where he was responsible for predicting physical traits—including 3D facial images—from the genome, as well as quantifying aging-related somatic changes in human genomes. Dr. Lippert also conducted research at Microsoft in Los Angeles, where he developed models for the genetic analysis of heritable phenotypes.