Potsdam, Germany
Oct. 17-18, 2018

Prof. Dr. Christoph Lippert

Chair of Digital Health & Machine Learning
Hasso Plattner Institute

As Chair of Digital Health & Machine Learning at the Hasso Plattner Institute, Lippert is exploring the theory of machine learning and artificial intelligence, as well as novel applications in medicine and genomics. The focus is on advancing the 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. Lippert has made important contributions to confounder correction in genomic association studies. Lippert studied bioinformatics from 2001–2008 in Munich and went on to earn his doctorate at the Max Planck Institutes for Intelligent Systems and for Developmental Biology in Tübingen on genome-associated studies. In 2012 he accepted a position in the US at Microsoft Research and subsequently carried out work at Human Longevity, Inc., a digital health venture founded by J. Craig Venter. In 2017 Lippert returned to Germany to head the research group "Statistical Genomics" at the Max Delbrück Center for Molecular Medicine in Berlin.

October 18, 2018
9:40am - 10:00am
Hasso Plattner Institute - Hörsaal 2

Technological advances in clinical measurement devices based on sequencing, imaging, and wearable sensors promise to accurately diagnose diseases in their earliest stages when they can be readily treated. Data science lies at the core of this vision of personalized medicine, where each individual is monitored based on their medical history, as well as their own genetic and environmental disease risk. While today, medicine is still mainly centered around treating symptoms rather than personalized treatment of disease mechanisms, recent cohort studies that pair genetics with multi-modal phenotyping will serve as reference populations to assess the relative disease risk of an individual, as they provide detailed longitudinal recordings of occurrence and progression of disease in healthy individuals.

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