Bio
Erwin Bottinger is the Co-Director of the Hasso Plattner Institute for Digital Health at Mount Sinai and Head of the Digital Health Center at the Hasso Plattner Institute. He holds dual academic appointments as chaired Professor for Digital Health - Personalized Medicine at the joint Digital Engineering Faculty of the Hasso Plattner Institute and University Potsdam in Germany, and as Professor of Medicine and Systems Pharmacology and Therapeutics at the Icahn School of Medicine at Mount Sinai in the USA. From November 2015 to July 2017 Erwin Bottinger was the CEO of the Berlin Institute of Health (BIH) where he played a key role in shaping its forward-looking strategy for 'Personalized Medicine - Advanced Therapies'.
Erwin Bottinger is considered an international expert in personalized medicine and digital health, in particular for his performance as founding director of the Charles Bronfman Institute for Personalized Medicine at the Icahn School of Medicine at Mount Sinai and the principal architect of the Institute’s BioMe™ Biobank, where he brought personalized medicine and digital health into clinical use.
Through his many years of research and leadership activities at leading academic medical institutions, such as Harvard Medical School, the National Cancer Research Institute (NCI), and the Icahn School of Medicine at Mount Sinai, the physician and scientist is proven for a global perspective on the future of medicine.
Research Focus
His research group addresses the challenges and opportunities of digital transformation to promote better health and health system. The focus lies on research and application of digital, sensor, and genomic technologies for precision health and personalized medicine.
Developing innovative methods for diagnosing health problems/predicting health outcomes in high-dimensional, heterogeneous data
Extracting information from disparate data sources and making information interoperable across diverse information systems
Analysis of large-scale electronic health record and clinical trials databases; e.g. retrieval, pipelines, and characterization of disease-specific cohorts, integration of EHR with genomic data, Data-driven identification of patient subgroups based on clinical and treatment process data
Developing artificial intelligence solutions to improve health outcomes through supporting better personal health decisions and better clinical care decisions