Stephanie C. Hicks, Ph.D.


Stephanie Hicks is an Assistant Professor in the Department of Biostatistics at Johns Hopkins Bloomberg School of Public Health. She is also a faculty member of the Johns Hopkins Data Science Lab and co-founder of R-Ladies Baltimore, a local chapter of a global organization to promote gender diversity in the R programming community. Her research interests are at the intersection of statistics, genomics, and data science. Broadly, she is focused on two major areas of research: (1) data science education and (2) developing statistical methods, tools, and software for the analysis of genomics data to improve quantification and our understanding of biological variability. In March 2019, she was highlighted in AMSTAT News along with 28 women to celebrate women in Statistics and Data Science for Women’s History Month.

Dr. Hicks grew up in a small town in North Louisiana and received her B.S. in Mathematics from Louisiana State University. Afterwards, she moved to Houston, Texas to complete her M.A. and Ph.D. in the Department of Statistics at Rice University under the direction of Marek Kimmel and Sharon Plon (@splon). She completed her postdoctoral training with Rafael Irizarry (@rafalab) in the Department of Data Sciences at Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health. Her postdoctoral work was awarded 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.

Single-cell RNA-Sequencing (scRNA-seq) data has become the most widely used high-throughput method for transcription profiling of individual cells. This technology has created an unprecedented opportunity to investigate important biological questions that can only be answered at the single-cell level. However, this technology also brings new statistical, computational and methodological challenges. To address these challenges, Dr. Hicks develops methods to address technical variability in single-cell data, methods for fast and scalable methods for clustering single-cell data, develops open-source, practical, reproducible, single-cell workflows to help researchers analyze their own data. She actively contributes software packages to the open-source and open-development R/Bioconductor software project and became involved in one of the 85 one-year projects to develop Collaborative Computational Tools partnering between the Chan Zuckerberg Initiative (CZI) and the Human Cell Atlas (HCA). With other Bioconductor developers, Dr. Hicks will create the infrastructure and tools needed to analyze potentially billions of single cells in the HCA within Bioconductor, which has been highlighted at Rice University and Johns Hopkins.

In addition, Dr. Hicks is passionate about identifying better ways to improve data science education, which she teaches at Johns Hopkins Bloomberg School of Public Health and previously taught at Harvard T.H. Chan School of Public Health.  An increase in demand for statistics and data science education has led to changes in curricula, specifically an increase in computing. While this has led to more applied courses, students still struggle with effectively deriving knowledge from data and solving real-world problems. To address this, her approach includes not only defining innovative frameworks to teach students to make important connections between the scientific question, data, and statistical concepts that only come from hands-on experience analyzing data, but also how to define the field from first principles, namely the elements and principles of data analysis, based on the activities of people who analyze data with a language and taxonomy for describing a data analysis in a manner spanning disciplines. Finally,  she is building the openDataCases community resource of case studies that educators can use in the classroom to teach students how to effectively derive knowledge from data.

Dr. Hicks is also actively working on creating a children’s book featuring trailblazing women in statistics and data science (stay tuned for updates!). She is most proud of her family — her incredibly supportive husband, Chris, and two beautiful boys. In many talks that she gives, Dr. Hicks talks about non-work–related things, such as her own hobbies or her family, as a way to normalize the stigma of work-life balance in academia. She strives hard every day to find that work-life balance and wants students to know you can absolutely have a great family life and be successful in academia.

You can find out more about the lab (and open postdoctoral scientist positions) at and you can follow Dr. Hicks on Twitter @stephaniehicks.

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