Computational Genomics
This research area is focused on the development and application of new computational methods for analyzing and interpreting genomic information. HGS faculty use advanced techniques from the fields of computer science and statistics – including machine learning, artificial intelligence, and causal inference – to improve human genome analysis for a number of important applications including variant detection, variant impact prediction, gene discovery, inferring how biological functions are encoded in the genome, disease risk prediction, single cell analysis, multi-omics data integration, and genomic privacy and encryption.
Faculty
Assistant Professor of Biomedical Informatics and Data Science; Affiliated Faculty, Yale Center for Genomic Health
Assistant Professor of Biomedical Informatics & Data Science and of Computer Science; Director of Graduate Admissions, Human Genome Sciences
Waldemar von Zedtwitz Professor of Medicine and Biomedical Informatics and Data Science; Deputy Dean for Biomedical Informatics; Chair, Department of Biomedical Informatics and Data Science
Associate Professor of Biomedical Informatics and Data Science
Stephen and Denise Adams Professor of Neurology & Director of the Stephen & Denise Adams Center for Parkinson’s Disease Research and Professor of Genetics and of Neuroscience; Academic Chief, Division of Movement Disorders, Neurology
Harvey and Kate Cushing Professor of Neurosurgery; Professor, Department of Neurosurgery
Ira V. Hiscock Professor of Biostatistics, Professor of Genetics and Professor of Statistics and Data Science