Mark Mixer

Mark Mixer is an Associate Professor in Applied Mathematics. His work emphasizes bridging theoretical concepts with real-world applications, particularly in algorithm design, data science education, and discrete geometry. He focuses on developing practical solutions for scheduling problems, sports analytics, and geometric modeling.

Research interests include algorithms for optimization, discrete geometry, machine learning applications, and curriculum development in data science. His recent work explores genetic algorithms for exam scheduling and polytope classification.

Publications span from 2010 to 2025, covering topics like polytope theory, sports data analysis, and calculus curriculum reform. No scientific awards or grants are explicitly mentioned.

No advising or team leadership roles are detailed in the provided text.