Machine learning algorithms for large biomedical datasets
The van Dijk Lab focuses on the development of new machine learning algorithms for large biomedical datasets. Their approaches aim to discover hidden structure, signals, and patterns in complex high-dimensional and high-throughput data including single-cell RNA sequencing, electronic health records, biomedical imaging, and brain activity recordings. In recent work, they have been using techniques from computer vision and natural language processing to encode large-scale spatio-temporal brain activity recordings, including mesoscopic calcium imaging and fMRI recordings.
Dr. van Dijk is an Assistant Professor of Medicine and of Computer Science. He received his PhD from the University of Amsterdam in Computer Science. Dr. van Dijk was Rubicon postdoctoral fellow at the Weizmann Institute of Science and Columbia University. In 2019 he opened a biomedical machine learning lab at Yale dept. of Internal Medicine and dept. of Computer Science. Dr. van Dijk is recipient of the NIH MIRA award. In his free time he enjoys hiking, building furniture, and making art.