Florence Moore 322
Dr. Thai Nhan is an Associate Professor of Mathematics at Menlo College. Prior to that, he was an Associate Professor and Math Program Director in the Department of Mathematics and Science at Holy Names University. He received his Ph.D. from the National University of Ireland Galway, mentored by Prof. Niall Madden (NUI Galway) and Prof. Scott MacLachlan (formerly at Tufts University, now at the Memorial University of Newfoundland). He has a wide range of research interests in Computational and Applied Mathematics including, but not limited to, Numerical Analysis, Numerical Linear Algebra, Scientific Computing, Biomath Modeling, and Math Education. His teaching philosophy is simple: “education is for everyone’’ and “everyone can learn mathematics’’.
• V. Mai, T.A. Nhan (2024). Fractional Modelling of H2O2-Assisted Oxidation by Spanish broom peroxidase. Mathematics. 2024; 12(9):1411.
• T.A. Nhan, R. Vulanovic (2023). Parameter-uniform convergence analysis on a Bakhvalov-type mesh with a smooth mesh-generating function using the preconditioning approach. Letters on Applied and Pure Mathematics, 1(2), 21-34.
• L. Claus, P. Ghysels, Y. Liu, T.A. Nhan, R. Thirumalaisamy, A.P.S. Bhalla (2023). Sparse Approximate multifrontal factorization with composite compression methods. ACM Transactions on Mathematical Software, Volume 24, Issue 3, Article No.: 24, pp 1-28.
• T.A. Nhan and Relja Vulanovic (2023). Analysis of a Second-order Hybrid Scheme on Bakhvalov-type meshes: the Truncation-error and Barrier-function Approach. Applied Numerical Mathematics, Vol 186, April 2023, pp. 84–99. “
• T.A. Nhan et al. (2023). A new upwind difference analysis of an exponentially graded Bakhvalov-type mesh for singularly perturbed elliptic convection-diffusion problems. Journal of Computational and Applied Mathematics, Vol. 418, Jan. 2023, 114622.”
• S. MacLachlan, N. Madden, and T.A. Nhan (2022). A Boundary-Layer Preconditioner for Singularly Perturbed Convection Diffusion. SIAM Journal on Matrix Analysis and Its Applications (SIMAX), Vol. 43 (2), 561–583.”
• T.A. Nhan and V. Mai (2022). A preconditioning-based analysis for a Bakhvalov-type mesh. In William McLean, Shev Macnamara, and Judith Bunder, editors, Proceedings of the 20th Biennial Computational Techniques and Applications Conference, CTAC-2020, volume 62 of ANZIAM J., pages C146–C162, February 2022.
• V. Mai, T.A. Nhan, Z. Hammouch (2021). A Mathematical Model of Enzymatic non-competitive inhibition by product and its applications. Physica Scripta 96 (2021) 124062.
• R. Vulanović and T.A. Nhan (2021). An Improved Kellogg-Tsan Solution Decomposition in Numerical Methods for Singularly Perturbed Convection-Diffusion Problems. Applied Numerical Mathematics, Volume 170, December 2021, Pages 128-145.
• R. Vulanović and T.A. Nhan (2021). T.A. Nhan and V. Mai (2021). On Bakhvalov-type meshes for a linear convection-diffusion problem in 2D. Mathematical Communications 26(2021), 121–130.
• T.A. Nhan (2021). A uniform convergence analysis for a Bakhvalov-type mesh with explicitly defined transition point. In: Garanzha V.A., Kamenski L., Si H. (eds) Numerical Geometry, Grid Generation and Scientific Computing. Lecture Notes in Computational Science and Engineering, Vol 143, pp 213–226. Springer, Cham. https://doi.org/10.1007/978-3-030-76798-3_13.
• T.A. Nhan (2021). V. Mai̇, T.A. Nhan (2021). Numerical analysis of coupled systems of ODEs and applications to enzymatic competitive inhibition by product . Advances in the Theory of Nonlinear Analysis and its Application, 5 (1), 58-71. DOI: 10.31197/atnaa.820590
• R. Vulanović and T.A. Nhan (2020). Robust hybrid schemes of higher order for singularly perturbed convection-diffusion problems. Applied Mathematics and Computation, Volume 386, 1 December 2020, 125495.
• R. Vulanović and T.A. Nhan (2020). Using the Kellogg-Tsan solution decomposition in numerical methods for singularly perturbed convection-diffusion problems. Numerical Analysis and Applicable Mathematics, 2020, 1(1), 1-9.
• T.A. Nhan and R. Vulanović (2020). The Bakhvalov mesh: a complete finite-difference analysis of two-dimensional singularly perturbed convection-diffusion problems. Numerical Algorithms 87, 203–-221 (2021). DOI: https://doi.org/10.1007/s11075-020-00964-z
• T.A. Nhan and N. Madden (2020). An analysis of diagonal and incomplete Cholesky preconditioners for a singularly perturbed problem on a layer-adapted mesh. Journal of Applied Mathematics and Computing 65, 245–272 (2021). https://doi.org/10.1007/s12190-020-01390-z
• L.P. Quan and T.A. Nhan (2020). A closed-form solution to the inverse problem in interpolation by a Bézier-spline curve. Arabian Journal of Mathematics 9, 155–-165 (2020). https://doi.org/10.1007/s40065-019-0241-0
• T.A. Nhan and R. Vulanović (2019). Analysis of the truncation error and barrier-functions technique for a Bakhvalov-type mesh. Electronic Transactions on Numerical Analysis (ETNA), Volume 51, Pages 315-330. (preprint)
• H. Nhan and T.A. Nhan (2019). Different Grouping Strategies for Cooperative Learning in English Majored Seniors and Juniors at Can Tho University, Vietnam. Education Sciences. 2019, 9, 59.
• T.A. Nhan (2018). Cooperative Learning Activities with a Focus on Geometry Applications in a Basic Math & Pre-Algebra Class. Bay Area Active Learning Workshop, 2018. (Mathematics Education)
• L.P. Quan and T.A. Nhan (2018). Applying Computer Algebra Systems in Approximating the Trigonometric Functions. Mathematical and Computational Applications. 2018; 23 (3):37.
• T.A. Nhan and R. Vulanović (2018). A note on a generalized Shishkin-type mesh, Novi Sad Journal of Mathematics. Vol. 48, No. 2, 2018, 141-150, 2018. DOI: https://doi.org/10.30755/NSJOM.07880
• T.A. Nhan, M. Stynes, and R. Vulanović (2018). Optimal Uniform-Convergence Results for Convection-Diffusion Problems in One Dimension Using Preconditioning. Journal of Computational and Applied Mathematics, 2018.
• T.A. Nhan, S. MacLachlan, and N. Madden (2018). Boundary layer preconditioners for finite-element discretizations of singularly perturbed reaction-diffusion problems. Numerical Algorithms, 2017. (Cite as: Nhan, T.A., MacLachlan, S. & Madden, N. Numer Algor (2018) 79: 281. https://doi.org/10.1007/s11075-
• Summer 2024: The U.S. Department of Energy (DOE) Visiting Faculty Program (VFP) at the Lawrence Berkeley National Laboratory (LBNL), funded by the DOE, Office of Science ($27,300 per ten weeks).
• “Summer 2023: The U.S. Department of Energy (DOE) Visiting Faculty Program (VFP) at the Lawrence Berkeley National Laboratory (LBNL), funded by the DOE, Office of Science ($20,000 per ten weeks).,
• Summer 2022: The U.S. Department of Energy (DOE) Visiting Faculty Program (VFP) at the Lawrence Berkeley National Laboratory (LBNL), funded by the DOE, Office of Science ($17,500 per ten weeks).,
• The FernUniversität in Hagen President’s Travel Award to attend the 18th Workshop on Numerical Methods for Problems with Layer Phenomena, 24–26 March 2022 in Hagen, Germany.
• Summer 2019, Faculty Trainee, Google New York Headquarter, The Google Applied Computing Series project: the pilot program for two college-level introductory computer science and data science courses and a machine learning intensive. (Skills trained Python, NumPy, SQL).
• The Travel Award for junior researchers to attend the Conference on Computational Mathematics and Applications, University of Nevada Las Vegas, Oct. 2019.
• The Travel Award (from Lawrence Berkeley Lab) for junior researchers to attend the International Conference on the Preconditioning Techniques for Scientific and Industrial Applications, University of Minnesota, Jul. 2019.
• b The National Science Foundation (NSF) Travel Award for US junior researchers to attend the International Conference on the Preconditioning Techniques for Scientific and Industrial Applications, UBC, Vancouver, Aug. 2017.
• 2011–2015, Ph.D. project entitled: “Preconditioning techniques for singularly perturbed differential equations”. The project was funded by the Irish Research Council Grant No. RS/2011/179 (96,000 euros per four years).
• 2009–2011, M.Sc. project entitled: “Handling Nonlinearity and Uncertainty in Drug Delivery Modelling” funded by Science Foundation Ireland (SFI) under their Research Frontiers Programme grant SFI RFP/CMS/1254 (37,500 euros per 18 months).
