Victorita Dolean Maini

Victorita Dolean Maini is a Visiting Professor in the Department of Mathematics and Statistics at the University of Strathclyde, Faculty of Science. She is actively engaged in research and supervision, with a focus on computational science and numerical methods for partial differential equations. Her work bridges applied mathematics, high-performance computing, and interdisciplinary applications in geophysics, biomedical engineering, and pharmaceutical modeling.

• University: University of Strathclyde
• School: Faculty of Science
• Department: Mathematics and Statistics
• Academic Rank: Visiting Professor

Her research interests center on computational science, particularly in developing mathematical models and algorithms for complex physical systems governed by partial differential equations. She specializes in domain decomposition methods, iterative solvers, and high-performance computing, with recent extensions into scientific machine learning and physics-informed neural networks. Her work emphasizes rigorous analysis and validation of numerical results.

The most recent publications highlight a strong trend in robust and scalable numerical methods for multiscale and multiphysics problems. Topics include domain decomposition with GenEO coarse spaces, optimized transmission conditions for diffusion, wave propagation in anisotropic media, and computational epidemiology. These works span disciplines such as applied mathematics, computational physics, geophysics, and biomedical modeling, reflecting a highly interdisciplinary approach. There is a clear emphasis on industrial and real-world applications, including seismic imaging, crystallization processes, and hemodynamic simulations.

Scientific awards include:

• Fellow (awarded 7 September 2020)
• Prix Bull-Joseph Fourier 2015 (awarded 12 April 2016)

She has been a co-investigator and principal investigator on multiple research grants, including projects like PharmaCrystNet, Fast solvers for frequency domain wave-scattering, and Blood flow dynamics in pulmonary hypertension. She actively supervises PhD students and collaborates internationally. She has organized key seminars and workshops, particularly in scientific machine learning and physics-informed learning, contributing significantly to academic community building.

She leads and participates in research teams focused on computational modeling, numerical linear algebra, and interdisciplinary applications. Her group collaborates with institutions in physics, engineering, and life sciences, leveraging high-performance computing for large-scale simulations. She is also involved in promoting diversity through initiatives like the Women in Data Science and Mathematics Seminar Series.