Research interests
- Statistical and computational methodology in genetics, cancer biology, metagenomics, and morphometrics;
- Bayesian methodology for high-dimensional and complex data;
- Machine learning algorithms for the analysis of massive biological data;
- Integration of statistical inference with differential geometry and algebraic topology;
- Stochastic topology;
- Discrete Hodge theory;
- Inference in dynamical systems
Additional Profiles & Links:
- Sayan Mukherjee Research Page
- Duke Mathematics Department
- Duke center for Genomic and Computational Biology
- Scholars@Duke
