- Genetics of complex traits
- Use of machine learning and probabilistic models to untangle the effects of genetic variation on clinically relevant phenotypes
- Graphical models
- Transfer learning
- Structured regularization methods
Alexis Battle’s research focuses on unraveling the impact of genetics on the human health, 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, leveraging diverse data to infer more comprehensive models of genetic effects on the cell. 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 JHU in July 2014.