Research & Publications
Dr. David van Dijk is Assistant Professor at Yale Dept. of Internal Medicine and Yale Dept. of Computer Science where he leads a research group that focuses on the cutting-edge application of machine learning methods to big biomedical data. His group develops new algorithms for discovering hidden structure, signals, and patterns in complex high-dimensional and high-throughput data, including single-cell RNA sequencing, microbiome, medical imaging, and electronic health records. His research team comprises trainees from diverse backgrounds, including computer science, mathematics, physics, biology, medicine, and neuroscience. Dr. van Dijk completed his PhD in Computer Science at the University of Amsterdam and the Weizmann Institute of Science, where he used machine learning to understand how gene regulation is encoded in DNA sequence. As a postdoc at Yale Genetics and Computer Science, he developed machine learning methods for single-cell data that are widely used in the biomedical community.
Education & Training
- PhDUniversity of Amsterdam, Computer Science (2013)
- MScFree University of Amsterdam, Computer Science (2008)
- BScFree University of Amsterdam, Computer Science (2005)