Dr. Jason Ernst

Biography

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

Research Interests

Experience

Awards