I am a postdoctoral researcher funded by a K99/R00 Pathway to Independence Award from the NIH NHGRI in the Department of Biostatistics at Johns Hopkins Bloomberg School of Public Health, advised by Dr. Stephanie Hicks.

My work in on the development of unsupervised statistical methodology and software for analyzing data from spatial transcriptomics, single-cell RNA sequencing, and other high-throughput genomics technologies. I implement these methods as freely available R packages within the open-source Bioconductor project.

My methodological work is motivated by collaborative projects with experimental researchers in fields including neuroscience and cancer. I strongly support open science principles including the release of open-source software, reproducible analyses, free availability of code and data resources, and publication of preprints. I also support rigorous benchmarking against existing methods during development work. I enjoy teaching, and have been an instructor for several short workshops on R programming and data science skills through the Data Carpentry and Software Carpentry initiatives. In January 2021, I received a Research Symbiont award in recognition of my efforts in sharing open code and data resources.

My training includes a PhD in Biostatistics with Dr. Mark Robinson at the University of Zurich, Switzerland, and a MSc in Statistics at ETH Zurich, Switzerland. Previously, I studied physics, mathematics, and economics at the University of Western Australia and Australian National University, and worked as a policy analyst for the Australian federal public service.