Alexis Battle

Methods for detecting context-specific and dynamic effects of genetic variation

Short Bio.Alexis Battleā€™s research focuses on unraveling the impact of genetics on the human body, using machine learning and probabilistic methods to analyze large scale genomic data. She is interested in applications to personal genomics, genetics of gene expression, and gene networks in disease. She earned her Ph.D. and Masters in Computer Science in 2014 from Stanford University in 2014, where she also received her Bachelors in Symbolic Systems (2003). Alexis also spent several years in industry as a member of the technical staff at Google. Prior to joining Hopkins, Alexis spent a year as a postdoc with Jonathan Pritchard with HHMI and the Genetics Department at Stanford. She joined John’s Hopkins in July 2014 and is currently an Assistant Professor in the Departments of Biomedical Engineering and Computer Science. Alexis was named a 2016 Searle Scholar and is a Johns Hopkins University Catalyst award recipient.