Statistical inference for molecular discovery
We develop and apply new statistical algorithms for biological inference on fundamental questions in the evolution of genomes.
We are currently focusing on developing and applying a new generation of algorithms for genomics, based on performing direct inference on raw sequencing data. The first of these algorithms is called SPLASH [GitHub: SPLASH]
SPLASH can be applied to diverse biological problems including discovery in microbial, viral, and eukaryotes using DNA or RNA sequencing data.
SPLASH performs tasks where reference genomes exist and tools for studying them well-developed and where reference genomes do not exist [hyperlinkTAB under construction: V(D)J, splicing, CCLE]
Our ultimate goal is to contribute to unifying models for genomic evolution, regulation and function at the macro and micro scales.
Our work includes studies of metagenomics and microbiology, plants, and other non-model organisms, especially those with relevance to planetary health
We are committed to fostering an interdisciplinary, inclusive environment and are actively recruiting undergraduates, graduate students, and postdoctoral fellows.
We recognize that Stanford sits on the ancestral land of the Muwekma Ohlone Tribe. This land was and continues to be of great importance to the Ohlone people. Consistent with our values of community and inclusion, we have a responsibility to acknowledge, honor and make visible the university’s relationship to Native peoples.
We are currently recruiting talented undergraduate, graduate students, medical research fellows and post-docs to join our research efforts.