Profile
Dr. Jason Ernst develops and applies computational methods to improve the analysis of genomic data collected from cells. He entered the field of computational biology as a PhD student studying machine learning during the emergence of the broad use of high-throughput functional genomics assays. His novel computing approaches enable researchers to effectively analyze and interpret the massive amounts of data generated when cells are studied using high-throughput genomic technologies. Dr. Ernst’s methods are leading to a better understanding of gene regulation and the epigenome, and key insights into regions of the genome associated with common diseases. He also is developing computational approaches that utilize machine learning to analyze epigenomic and other high-throughput data to understand diseases associated with the non-coding portion of the human genome. He applies his approaches in collaboration with colleagues to understand diseases such as schizophrenia, bipolar disorder, autism, and melanoma. Dr. Ernst earned a doctorate from the School of Computer Science at Carnegie Mellon University and completed postdoctoral training at the Massachusetts Institute of Technology.
Prof. Ernst recently received a five-year grant by NIH’s National Human Genome Research Institute as part of its newly established Impact of Genomic Variation on Function (IGVF) Consortium.