معرفی
Mireille Mimi Boutin is an Associate Professor of Electrical and Computer Engineering at Purdue University, affiliated with the Elmore Family School of Electrical and Computer Engineering and the Department of Mathematics. She holds a BSc from the University of Montreal (1996) and a PhD from the University of Minnesota (2001). Her research spans applied mathematics, signal processing, machine learning, and interdisciplinary fields such as nutritional management and education research. Key areas include computer vision, discrete inverse problems, and data science applications in education. She has contributed to methods for shape recognition, symmetry detection, and phenylalanine estimation in foods, with applications in metabolic disease management. Her work also explores innovative teaching methods like Project Rhea and slectures to enhance student engagement. Boutin serves as an associate editor for La Matematica and the SIAM Journal on Applied Algebra and Geometry. Her labs include the Data Science Labs for Calculus and research projects on classification algorithms and structure from motion. Current and past students include graduate researchers, visiting scholars, and undergraduates. Office hours and contact details are available via her website and the MATH department.
Education: BSc (1996), University of Montreal; PhD (2001), University of Minnesota.
Research focuses on invariant-based methods, cluster analysis in high-dimensional data, and mathematical approaches to nutrition. Notable contributions include reconstructing room shapes from acoustics and developing machine learning tools for educational data analysis. Recent publications address clusterability benchmarks, phenylalanine estimation, and pedagogical innovations.
Grants and collaborations include work on dietary management algorithms and educational technology. Labs and teams involve interdisciplinary efforts in mathematics, engineering, and computer science. Future works emphasize expanding applications in healthcare and education through advanced data-driven methodologies.
