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Assistant Professor This email address is being protected from spambots. You need JavaScript enabled to view it.
(205) 934-2154
University Hall 4045

Research interest: Uncertainty Quantification of Stochastic PDEs (which includes Navier-Stokes Equations (NSE), Magnetohydrodynamics (MHD), and other Multiphysics problems) with Fast and Efficient Algorithms; Reduced Order Modeling (ROM); Population Dynamics; Applied Analysis.

Teaching interest: Numerical Analysis and Scientific Computing, Numerical Linear Algebra, and Partial Differential Equations.

Office Hours: Monday and Wednesday 11:10 am-12:10 pm or by appointment.

Education: 

  • B.S., University of Dhaka, Mathematics
  • M.S., University of Dhaka, Applied Mathematics
  • M.S., Clemson University, Applied and Computational Mathematics
  • Ph.D., Clemson University, Applied and Computational Mathematics

After graduation, I worked as a joint postdoc at the Virginia Tech, Department of Mathematics, and Department of Biomedical Engineering & Mechanics, where I taught mathematics courses and collaborated with faculty members in the data-driven ROM of fluid flows and soft-tissue deformation simulation. Then, I worked at the Massachusetts Institute of Technology (MIT), Plasma Science and Fusion Center (PSFC) as a postdoc. At MIT-PSFC, I collaborated with the modeling and simulation group to build a robust Krylov space-based solver for simulating the magnetic field produced by the high-temperature non-insulated superconducting magnet in the SPARC tokamak which is going to be launched soon by MIT and Commonwealth Fusion Systems.

Personal Website Opens an external link.

  • Select Publications
    • Decoupled algorithms for non-linearly coupled reaction-diffusion competition model with harvesting and stocking,  Mohebujjaman, C. Buenrostro, M. Kamrujjaman, T. Khan, Journal of Computational and Applied Mathematics, 436,115421, 2024.
    • Scalability analysis of direct and iterative solvers used to model charging of superconducting pancake solenoids,  Mohebujjaman, S. Shiraiwa, B. LaBombard, J. Wright, and K. K. Uppalapati, Engineering Research Express, 5(1), 015045, 2023.
    • High order efficient algorithm for computation of MHD flow ensemble,  Mohebujjaman, Advances in Applied Mathematics and Mechanics, 4, 1111-1137, 2022.
    • An efficient algorithm for parameterized magnetohydrodynamic flow ensembles simulation,  Mohebujjaman, H. Wang, and L. Rebholz, Computers and Mathematics with Applications, 112, 167-180, 2022.
    • Commutation Error in Reduced Order Modeling of Fluid Flows, B. Koc,  Mohebujjaman, C. Mou, and T. Iliescu, Advances in Computational Mathematics, 45, 2587-2621, 2019.
    • An evolve-filter-relax stabilized reduced order stochastic collocation method for the time-dependent Navier-Stokes Equations, M. Gunzburger, T. Iliescu,  Mohebujjaman, and M. Schneier, SIAM/ASA Journal on Uncertainty Quantification, 7(4), 1162-1184, 2019.
    • Physically-Constrained Data-Driven Correction for Reduced Order Modeling of Fluid Flows,  Mohebujjaman, L. G. Rebholz, and T. Iliescu, International Journal for Numerical Methods in Fluids, 89(3), 103-122, 2019.
    • Data-Driven Filtered Reduced Order Modeling of Fluid Flows, X. Xie,  Mohebujjaman, L.G. Rebholz, and T. Iliescu, SIAM Journal on Scientific Computing, 40(3), B834-B857, 2018.
    • Energy Balance and Mass Conservation in Reduced Order Models of Fluid Flows,  Mohebujjaman, L.G. Rebholz, X. Xie, and T. Iliescu, Journal of Computational Physics, 321, 128-142, 2017.
    • High order algebraic splitting for magnetohydrodynamics simulation, M. Akbas,  Mohebujjaman, L. Rebholz, and M. Xiao, Journal of Computational and Applied Mathematics, 321, 128-142, 2017. 
    • Decoupled, unconditionally stable, higher order discretizations for MHD flow simulation, T. Heister,  Mohebujjamanand L. G. Rebholz, Journal of Scientific Computing, 71(1), 21-43, 2017.
    • An efficient algorithm for computation of MHD flow ensembles,  Mohebujjamanand L. G. Rebholz, Computational Methods in Applied Mathematics, 17(1), 121-137, 2017.
    • Analysis of a family of optimally accurate regularization methods for Navier-Stokes equations, N. Jiang,  Mohebujjaman, L. Rebholz and C. Trenchea, Computer Methods in Applied Mechanics and Engineering, 310, 388-405, 2016.
    • Numerical analysis and testing of a fully discrete, decoupled penalty-projection algorithm for MHD in Elsasser variable, M. Akbas, S. Kaya,  Mohebujjamanand Leo G. Rebholz, International Journal of Numerical Analysis and Modeling, 13(1), 90-113, 2016.