Stephanie Hicks, “Scalable Computational Methods and Software for Single-Cell and Spatial Data Science”
Please join us on Tuesday, September 12, 2023 at 12:00pm in Clark Hall Room 110 and on ZOOM for the
CIS & MINDS Seminar Series:
Guest: Stephanie Hicks
Associate Professor
Johns Hopkins University
Topic: “Scalable computational methods and software for single-cell and spatial data science”
Join Zoom Meeting: https://wse.zoom.us/j/95386212146
Stephanie Hicks
Associate Professor, Biostatistics
Johns Hopkins University
“Scalable computational methods and software for single-cell and spatial data science”
Abstract: Single-cell RNA-Seq (scRNA-seq) is the most widely used high-throughput technology to measure genome-wide gene expression at the single-cell level. However, single-cell data present unique challenges that have required the development of specialized methods and software infrastructure to successfully derive biological insights. Compared to bulk RNA-seq, there is an increased scale of the number of observations (or cells) that are measured and there is increased sparsity of the data, or fraction of observed zeros. Furthermore, as single-cell technologies mature, the increasing complexity and volume of data require fundamental changes in data access, management, and infrastructure alongside specialized methods to facilitate scalable analyses. I will discuss some challenges in the analysis of scRNA-seq and spatially-resolved transcriptomics data and present some solutions that we have made towards addressing these challenges.
Biography: Dr. Stephanie Hicks is an Associate Professor in the Department of Biostatistics at Johns Hopkins BSPH where she is also a faculty member of the Johns Hopkins Data Science Lab, and has 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. She is an expert in developing scalable computational methods and open-source software for biomedical data analysis, in particular single-cell and spatial transcriptomics genomics data, leading to an improved understanding of human health and disease. She serves on a variety of boards including the Bioconductor Technical Advisory Board, and the Editorial Board at Genome Biology and the Journal of American Statistical Association. Locally, she co-founded and co-organizes the R-Ladies Baltimore chapter to promote gender diversity in the R programming language community. She is a recipient of several professional awards including a K99/R00 Pathway to Independence Award, a High-Impact Project Award from the Bloomberg American Health Initiative, Teaching in the Health Sciences Young Investigator Award from the American Statistical Association (ASA), and the COPSS Emerging Leader Award from the ASA, arguably the statistical profession’s most prestigious award for early career leaders in Statistics and Data Science.