James Quinlan
Assistant Professor
Education
- PhD, Computational Science, Univ. of Southern Mississippi
- PhD, Mathematics Education, Ohio State University
- MS, Mathematics, Youngstown State University
- BS, Mathematics, Ohio State University
Current Courses
COS 485 Algorithm Design and Analysis
COS 470: Mathematics of Deep Learning
<!-- -->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. This work has applications in 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. The Northeastern Section of the Mathematical Association of America (MAA) awarded him the 2023 Distinguished University Teaching Award for his contributions inside and outside the classroom. Subsequently, he was nominated for the Deborah and Franklin Tepper Haimo Awards for Distinguished Teaching (the highest teaching honor bestowed by the MAA).
Dr. Quinlan’s leadership and expertise extend beyond the classroom. He has played a pivotal role in program development, notably contributing to creating one of the nation’s first data science programs. His efforts also helped establish a fully online graduate program in data science at the University of Rhode Island. In addition to his contributions to numerical analysis and scientific computing, Dr. Quinlan has designed and taught various computer science, mathematics, and data science courses, covering essential topics such as data structures and algorithms, database design and implementation, and data exploration in R.
Dr. Quinlan is currently the co-editor of the North American GeoGebra Journal and is involved in the TEA Journal Project, a global collaboration of 60 authors worldwide. This project fostered six research groups dedicated to researching the role of artificial intelligence in computer science and mathematics education. Dr. Quinlan is a past member of the MAA’s national Committee on Technologies in Mathematics Education (CTME), and 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.
Refereed Journals
Hunhold, L. and Quinlan, J. (2025). Evaluation of Bfloat16, Posit, and Takum Arithmetics in Sparse Linear Solvers. ARITH 2025. In 2025 IEEE 32st Symposium on Computer Arithmetic (ARITH) (pp. forthcoming).. IEEE.
Quinlan, J. and Omtzigt, E.T.L. (2024). Iterative Refinement with Mixed-Precision Posit Arithmetic. In: Gustafson, J., Dimitrov, V. (eds) Next Generation Arithmetic. CoNGA 2024. Lecture Notes in Computer Science. Springer.
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.
Quinlan, J. (2023). Efficacy and Attitudes Towards Online Homework Systems in First-Semester Calculus. Ohio Journal of School Mathematics, 95(1), 26–31.
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
Quinlan, J. (2024, April). Engaging Students in and out of the classroom. Department of Mathematical Science, Bentley University. (Invited Talk).
Quinlan, J., & Omtzigt, E. T. L. (2024, Feburary). Low Precision Iterative Refinement. Conference on Next Generation Arithmetic (CoNGA'24). National Supercomputing Centre, Singapore.
Quinlan, J. (2023, November). After the Bell Rings: Engaging Students Outside the Classroom. Mathematics Association of America (MAA) Northeastern Section, (Invited Talk). Boston College, MA.
Chamberlain, D., & Quinlan, J. (2023, August). Technology Use in Undergraduate Mathematics Classrooms. Mathematics Association of America (MAA), Contributed Paper Session MathFest 2023. Tampa, FL.
Quinlan, J., & Omtzigt, E. T. L. (2023, June). Universal Numbers Library. MAA Northeastern Regional Conference, Fitchburg, MA.
Quinlan, J. (2023, March). Data Mining Methods for Improving Health Outcomes. MaineR Users Group. Northeastern University Roux Institute, Portland, Maine.
Education
- PhD, Computational Science, Univ. of Southern Mississippi
- PhD, Mathematics Education, Ohio State University
- MS, Mathematics, Youngstown State University
- BS, Mathematics, Ohio State University
Current Courses
COS 485 Algorithm Design and Analysis
COS 470: Mathematics of Deep Learning
<!-- -->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.