Bio: My name is Reginald (RB) McGee, and I am currently an associate professor of mathematics and statistics at Haverford College. I was born, raised, and public-school-educated in Saginaw, Michigan and then attended Florida A&M University as an undergraduate. I completed my Ph.D. in Mathematics at Purdue University and then completed a three-year postdoctoral fellowship at the Mathematical Biosciences Institute at The Ohio State University. My research program considers computational biology and bioinformatics, and I often investigate the interplay between models and biomedical data. My primary scientific interests lie in blood diseases and health disparities.

Additional information can be found here.

Topics include:

Singled Out: Analyzing single-cell data to identify significant interactions in leukemia
Complex protein interaction networks complicate the understanding of what most promotes the rate of cancer progression. High dimensional data provides opportunities for new insights into possible mechanisms for the proliferative nature of aggressive cancers, but these datasets often require fresh techniques and ideas for exploration and analysis. In this talk, we consider mass cytometry data capturing expression levels of tens of biomarkers in individual cells from acute myeloid leukemia patients. After identifying immune cell subpopulations in this data using an established clustering method, we present a novel statistic for testing differential biomarker correlations across patients and within specific cell phenotypes.

Orthogonal Polynomials in Scientific Computing
In linear algebra, we learn many benefits to finding a basis for a vector space, and when there is an orthogonal basis available it brings a smile to your face like a 50 degree day. When the vector space of interest is the collection of polynomials, we can apply the Gram-Schmidt process to the monomials and arrive at a basis of pairwise orthogonal polynomials. Interestingly, we also find that orthogonal polynomials arise through other means such as recurrence relations and as solutions of differential equations. In this talk, we will discuss popular families of orthogonal polynomials and look at how they help get to the root of fundamental tasks in scientific computing such as polynomial interpolation and numerical integration.