James Quinlan
Assistant Professor of Computer Science
Education
Ph.D., Computational Science, Univ. of Southern Mississippi
Ph.D., Mathematics Education, Ohio State University, Columbus, Ohio
M.S., Mathematics, Youngstown State University, Youngstown, Ohio
B.S., Mathematics, Ohio State University, Columbus, Ohio
Current Courses
Research Interests
My current research interest is numerical linear algebra for high-performance computing. The focus is the design, analysis, implementation, and experimental evaluation of software and algorithms that use low-precision arithmetic to address the fundamental problem of solving systems of linear equations, Ax = b, that arise in a wide range of science and engineering applications.
James Quinlan joined the department in August 2023. He received his Ph.D. in Computational Sciences under the direction of James Lambers, where he developed a stencil selection algorithm with an adaptive mesh refinement method for upscaling transmissibility in subsurface flow simulations. Applications of this work include controlling groundwater contamination and predicting sequestered carbon dioxide escape rates.
His current research interest is numerical linear algebra for high-performance computing and deep learning.
Dr. Quinlan is a recognized educator awarded the 2023 Distinguished University Teaching Award by the Northeastern Section of the Mathematical Association of America (MAA) for his contributions inside and outside the classroom and was subsequently nominated for the Deborah and Franklin Tepper Haimo Awards for Distinguished Teaching (highest teaching honor bestowed by the MAA).
Dr. Quinlan serves as editor of the North American GeoGebra Journal, is a member of the MAA’s national Committee on Technologies in Mathematics Education (CTME), and is the webmaster for its regional section.
Selected Publications
Books
Lambers, J. V., Mooney, A. S., Montiforte, V. A., & Quinlan, J. (2024, forthcoming). Explorations in numerical analysis and deep learning with Julia. World Scientific.
Quinlan, J. (2021, in progress). Database Concepts, Cloud Computing, and Big Data.
Refereed Journals
Quinlan, J. and Edwards, T. (2024). On the Even Distribution of Odd Primes: An on-ramp to mathematical research. The Mathematics Enthusiast, 21(1&2), 327 - 334.
Omtzigt, E.T.L. and Quinlan, J. (2023). Universal Numbers Library: Multi-format Variable Precision Arithmetic Library. Journal of Open Source Software, 8(83), 5072.
Omtzigt, E.T.L. and Quinlan, J. (2022). Universal: Reliable, Reproducible, and Energy-Efficient Numerics. In: Gustafson, J., Dimitrov, V. (eds) Next Generation Arithmetic. CoNGA 2022. Lecture Notes in Computer Science, 13253. Springer.
Recent Presentations
Chamberlain, D., & Quinlan, J. (2023, August). Technology Use in Undergraduate Matheamtics Classrooms. Mathematics Association of America (MAA), Contributed Paper Session MathFest 2023. Tampa, FL.
Education
Ph.D., Computational Science, Univ. of Southern Mississippi
Ph.D., Mathematics Education, Ohio State University, Columbus, Ohio
M.S., Mathematics, Youngstown State University, Youngstown, Ohio
B.S., Mathematics, Ohio State University, Columbus, Ohio
Current Courses
Research Interests
My current research interest is numerical linear algebra for high-performance computing. The focus is the design, analysis, implementation, and experimental evaluation of software and algorithms that use low-precision arithmetic to address the fundamental problem of solving systems of linear equations, Ax = b, that arise in a wide range of science and engineering applications.