Michel Dumontier

Profile

Dr. Michel Dumontier is a Distinguished Professor of Data Science at Maastricht University. His research focuses on the development of computational methods for scalable integration and reproducible analysis of FAIR (Findable, Accessible, Interoperable and Reusable) data across scales - from molecules, tissues, organs, individuals, populations to the environment. His group combines semantic web technologies with effective indexing, machine learning and network analysis for drug discovery and personalized medicine. Previously at Stanford University, Dr. Dumontier now leads a new inter-faculty Institute for Data Science at Maastricht University with a focus on accelerating scientific discovery, improving health and well-being, and strengthening communities. He is a Principal Investigator for the NCATS Biomedical Data Translator and a co-Investigator for the NIH BD2K Center for Expanded Data Annotation and Retrieval (CEDAR). He is a technical lead for the FAIR (Findable, Accessible, Interoperable, Re-usable) data initiative, and is the scientific director for Bio2RDF, an open source project to generate Linked Data for the Life Sciences. He is the editor-in-chief for the IOS press journal Data Science and an associate editor for the IOS press journal Semantic Web. He is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies as evidenced by awards, keynote talks at international conferences, and collaborations on international projects.

Accelerating scientific discovery with FAIR

With its focus on investigating the basis for the sustained existence of living systems, modern biology as always been a fertile, if not challenging, domain to represent knowledge amenable to computational-based discovery. Indeed, the existence of millions of scientific articles, thousands of databases, and hundreds of ontologies, offers an exciting opportunity to reuse our collective knowledge, were we not stymied by incompatible formats, partial and overlapping standards, and heterogeneous data access. In this talk, I will discuss our efforts to develop computational frameworks and methods to wrangle knowledge into simple, but effective representations and to make these FAIR - Findable, Accessible, Interoperable, and Reuseable. Our work sets the stage for a global revolution to take advantage of the data we already have and to increase our confidence and the evidence in reporting, validating, and generating novel scientific discoveries.