I am an applied statistician who develops fast, scalable, statistical methodology, and open-source software for biomedical data analysis, which often contains noisy or missing data and systematic biases. Specifically, my research addresses statistical and computational challenges in single-cell genomics, epigenomics, and spatial transcriptomics leading to an improved understanding of human health and disease. My research philosophy is problem-forward: I develop statistical methods and software that are motivated by concrete problems, often with real-world, messy data. This philosophy permeates into my contributions to statistics and data science education, and service to the profession. For example, I also develop resources for data science education motivated by real-world problems and messy data and I serve my professional community at large, and being an advocate for diversity, equity and inclusion.
I am an Associate Professor in the Department of Biostatistics at Johns Hopkins Bloomberg School of Public Health, a faculty member of the Johns Hopkins Data Science Lab, and have affiliations with the Malone Center for Engineering in Healthcare, Center for Computational Biology, the Department of Genetic Medicine, and the Department of Biochemistry and Molecular Biology. I’m also a co-host of the The Corresponding Author podcast, member of the Editorial Board for Genome Biology, an Associate Editor for Reproducibility at the Journal of the American Statistical Association, and co-founder of R-Ladies Baltimore.
I completed my postdoctoral training with Rafael Irizarry (@rafalab) in the Department of Data Science at Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health. This postdoctoral research resulted in a K99/R00 grant from the National Human Genome Research Institute (NHGRI) (@genome_gov) to develop statistical methods for the normalization and quantification of single-cell RNA-sequencing data (R00HG009007).
Currently, I co-lead as a Principal Investigator a R01 grant from the National Institute of Mental Health (@NIMHgov) to develop improved statistical methods for the integrative cellular deconvolution of human brain RNA sequencing data (R01MH13183).
Most recently, I was selected for the Teaching in the Health Sciences Young Investigator Award and the COPSS Emerging Leader Award (formerly known as the COPSS Leadership Academy Award) from the American Statistical Association (ASA), arguably the statistical profession’s most prestigious award for early career leaders in Statistics and Data Science.
For more information, my full CV can be viewed on Overleaf here.