Rizwan Khan

Skilled researcher with expertise in pure and applied mathematics, with a strong background in differential geometry, dynamical systems, homotopy theory, algebra, analysis, algebraic topology, and fluid mechanics. Research interests focus on development of advanced mathematical models of complex physical and biological systems, especially magnetic field-conductive hybrid nanofluids. Involved in modelling Prandtl–Eyring fuzzy hybrid nanofluids and embedding Scientific Machine Learning (SciML) in hybrid models combining physics-based differential equations and data-based methods for systems biology and epidemiology applications.

Implementing advanced artificial neural network techniques with optimization methods including scaled conjugate gradient (SCG), Levenberg–Marquardt algorithm (LMA), and Bayesian regularization (BR). Future research focuses on understanding coupling and elimination of singularities in Bott–Morse foliations, modelling non-Newtonian biological fluids, thermal radiation effects in biological tissues, and drug delivery simulation with nanoparticles.

Ph.D. in Mathematics – Institute of Mathematics, Federal University of Rio de Janeiro Brazil (IM-UFRJ) (2011-09 – 2016-02)

Master of Mathematics in Mathematics – University of Peshawar, Pakistan (2005-09 – 2008-01)

B.Sc in Science – Government Superior science college Peshawar, Pakistan (2003-09 – 2005-08)

Professional Teaching Graduation in Education – Islamia College of education, Pakistan (2009-09 – 2010-09)