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Mark Chaisson is an Associate Professor in the Department of Biological Sciences at the University of Southern California. His research focuses on Quantitative Computational Biology, where he employs computational methods to understand complex biological systems. His work integrates techniques from statistics, machine learning, and bioinformatics to analyze large-scale biological data. Chaisson has contributed significantly to the field through various publications that explore topics such as genomic data analysis, protein structure prediction, and evolutionary biology. He is committed to educating students in computational biology and fostering their understanding of how computational tools can advance research in the biological sciences.
GRE is NOT required for Master's applicants for 2025-2026.