David van Dijk completed his PhD at the University of Amsterdam and the Weizmann Institute of Science (with Prof. Eran Segal) in Computer Science where he used machine learning to understand how gene regulation is encoded in DNA sequence. As a postdoctoral fellow at Yale Medical School and Dept. of Computer Science, he developed new machine learning and manifold learning methods for discovering hidden signatures in large biomedical data with an emphasis on single-cell data. David is currently an Assistant Professor in Medicine and in Computer Science at Yale, where he leads a research group in machine learning for biomedicine.
What do you do with data science?
The mission of our lab is to provide machine learning tools that extract meaningful insight from high-throughput, high-dimensional biomedical data. We work with data such as: Single-Cell RNA sequencing, Gut microbiome sequencing, Biomedical imaging, and Electronic Health Records. We are part of Internal Medicine (Cardiology) as well as Computer Science.