James Quinlan
Assistant Professor
Education
- PhD, Computational Science
Univ. of Southern Mississippi - MS, Mathematics
Youngstown State University - BS, Mathematics
Ohio State University
Current Courses
COS 280 Discrete Math II
COS 485 Algorithms
COS 470 Topics in CS
Office Hours:
M: 11:00 - 12:00 pm
T: 12:30 - 1:30
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.
Dr. Quinlan is an award-winning educator, receiving the 2023 Distinguished University Teaching Award from the Northeastern Section of the Mathematical Association of America (MAA). He was subsequently nominated for the MAA’s Deborah and Franklin Tepper Haimo Award for Distinguished Teaching.
His leadership extends beyond teaching and research. He has helped develop innovative data science programs, including one of the nation’s first undergraduate data science degrees and a fully online graduate program in data science at the University of Rhode Island. He has taught a broad range of courses in computer science, mathematics, and data science.
Dr. Quinlan is co-editor of the North American GeoGebra Journal, participates in the international TEA Journal Project exploring AI in mathematics and computer science education, and previously served on the MAA’s national Committee on Technologies in Mathematics Education.
Selected Publications
Books
Lambers, J. V., Mooney, A. S., Montiforte, V. A., & Quinlan, J. (2025). Explorations in numerical analysis and machine learning with Julia. World Scientific.
Refereed Journals
Quinlan, J. & Hunhold, L. (2026). Error Damping and Transfer Fidelity in Multigrids with Emerging Number Formats. In: Michalewicz, M., Gustafson, J., De Silva, H. (eds) Next Generation Arithmetic. CoNGA 2025. Lecture Notes in Computer Science. Springer.
Hunhold, L. & Quinlan, J. (2025). Numerical Performance of the Implicitly Restarted Arnoldi Method in OFP8, Bfloat16, Posit, and Takum Arithmetics. In SC25: International Conference for High Performance Computing, Networking, Storage and Analysis (pp. 681--694). IEEE. doi/10.1145/3712285.3759837
Hunhold, L. and Quinlan, J. (2025). Evaluation of Bfloat16, Posit, and Takum Arithmetics in Sparse Linear Solvers. In 2025 IEEE 32st Symposium on Computer Arithmetic (ARITH 2025). 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.
Software
Quinlan, J. & Arciero, M. (2026). UniversalNumbers.jl: Next-generation computer arithmetic in Julia (Version v0.1.3) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.21629613
Hunhold, L. & Quinlan, J. (2025). takum-arithmetic/MuFoLAB: v1.4.5 (Version v1.4.5) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.17708953
Recent Presentations
Quinlan, J. (2026, January). Leveraging Large Language Models for Course Design and Instructional Materials. Joint Mathematics Meetings (JMM). Washington D.C.
Quinlan, J. & Hunhold, L. (2025, May). Evaluation of Linear Solvers on Next Generation Arithmetic.. Mathematical Association of America Northeastern Section Spring Meeting.
Hunhold, L. & Quinlan, J. (2025, May). Evaluation of Bfloat16, Posit, and Takum Arithmetics in Sparse Linear Solvers. 2025 IEEE 32st Symposium on Computer Arithmetic (ARITH), El Paso, TX.
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.
Education
- PhD, Computational Science
Univ. of Southern Mississippi - MS, Mathematics
Youngstown State University - BS, Mathematics
Ohio State University
Current Courses
COS 280 Discrete Math II
COS 485 Algorithms
COS 470 Topics in CS
Office Hours:
M: 11:00 - 12:00 pm
T: 12:30 - 1:30
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.