Graduate School

Computational Biology Doctoral Candidate Smith Selected for Effectiveness in Teaching

Samuel Pattillo Smith, a doctoral candidate in Computational Biology, was nominated for his effectiveness in the virtual classroom and dedication to students.

Samuel Pattillo Smith
Samuel Pattillo Smith

“His availability to students outside of class and the investment he made in teaching effectively online are inspiring to me, as a faculty member who has taught Biol 0495 for over a decade,” shares nominator, Sohini Ramachandran, Hermon C. Bumpus Professor of Biology and Director of the Data Science Initiative. 

Smith also received high praise from those who took his courses. “Zoom is a difficult platform to teach on, but Sam was the single most organized professor that I have had during my online semesters at Brown. He seamlessly integrated class notes into lecture material and promptly posted things on Canvas,” shares one student nominator.

The course Smith taught, Statistical Analysis of Biological Data, is for students who have no background in either statistics or coding, but who learn to do both over the course of a semester. “It was such a special thing to watch students master learning two new languages, the shared notation of statistics and the daunting, rigorous syntax of a coding language such as R,” says Smith

Additionally, Smith made special efforts to integrate student research interests into his teaching, inviting fellow graduate students to present on how biostatistics is currently being used in their Research.

“I have always enjoyed teaching, but receiving this award is so affirming that the pedagogy I used was helpful for students. Ultimately, it feels incredibly validating that my ultimate goal of helping students learn a completely new concept was achieved,” says Smith.

For his dissertation, Smith is focused on how to develop and implement statistical methods that can help to overcome existing disparities in analyses of human genomic data. He has primarily worked on methods that use the aggregate effect of genetic mutations within a region to increase the statistical power to detect genetic determinants of clinically relevant traits.