معرفی
Matthew N. McCall is a tenured Associate Professor in the Department of Biostatistics and Computational Biology and the School of Medicine and Dentistry at the University of Rochester. He also holds an associate professorship in Biomedical Genetics and is affiliated with the Goergen Institute for Data Science. He serves as Director of the Statistics PhD Program, co-director of the Wilmot Cancer Institute Biostatistics and Bioinformatics Shared Resource, and associate director of the Environmental Health Biostatistics Training Grant.
His educational background includes a Ph.D. and M.H.S. in Biostatistics and Bioinformatics from Johns Hopkins Bloomberg School of Public Health (2010), and a B.S. in Statistics from the University of Michigan (2004).
McCall's research lies at the intersection of statistical genomics, bioinformatics, and systems biology. His work emphasizes the development of computational and statistical methods for analyzing high-throughput biological data, particularly in cancer and immunogenomics. Key areas include preprocessing and analysis of miRNA-seq and Perturb-seq data, modeling gene regulatory networks, adjusting for cellular composition in tissue-level expression studies, and analyzing microglia imaging data. His methodological work supports biomedical discovery through rigorous data science.
The recent publications reflect a strong focus on innovative statistical frameworks for single-cell and bulk sequencing data, with recurring themes in gene co-expression, non-coding RNA biology, and cancer systems biology. His work bridges computational innovation with biological insight, particularly in oncology and infectious disease.
While no specific awards are listed in the provided text, his leadership roles and consistent publication record in high-impact journals suggest recognition within the field.
McCall actively mentors graduate students and postdoctoral researchers, as evidenced by his supervision of PhD students and leadership of training grants. He is involved in significant research initiatives supported by institutional and federal funding, including the T32 training grant and shared resource leadership at the Wilmot Cancer Institute.
He leads a research group focused on statistical genomics, as seen from his GitHub presence (mccall-group), where tools like miRglmm are developed and shared. His lab integrates biostatistical theory with practical applications in biomedical research, fostering collaboration across disciplines.
