Monika Eisenmann

Monika Eisenmann is an Associate Senior Lecturer in the Department of Applied Mathematics at Lund University's Faculty of Engineering (LTH) and serves as a Principal Investigator at eSSENCE: The e-Science Collaboration. Her academic work bridges the fields of numerical analysis and probability theory, focusing on the development and analysis of stochastic numerical methods.

Dr. Eisenmann's research centers on stochastic numerical analysis, investigating how randomness influences mathematical problems from multiple perspectives. Her work examines stochastic elements within models themselves (accounting for uncertainties in parameters, natural variability, or external noise) as well as randomness introduced through numerical methods. She develops algorithms for solving stochastic differential equations and creates randomized approaches for optimization problems, with applications ranging from groundwater flow to machine learning frameworks.

Her research has significant implications for high-dimensional and low-regularity settings where traditional deterministic methods face challenges. By leveraging stochastic optimization techniques, her work helps avoid local minima while providing faster function evaluations in complex computational scenarios.

Dr. Eisenmann actively supervises doctoral students and leads multiple research projects:

• As primary supervisor for Marvin Jans (2023-2028)
• As joint second supervisor for Måns Williamson (2019-2025)
• Principal Investigator for "Stochastic numerical analysis with applications in groundwater flow problems" (Swedish Research Council, 2024-2028)
• Principal Investigator for "eSSENCE@LU 9:6 - Flow problems in porous media" (eSSENCE, 2023-2024)
• Researcher on "Moving domain decomposition methods for parabolic PDEs" (Swedish Research Council, 2024-2028)
• Researcher on "Analysis of numerical methods for optimization problems arising in machine learning" (2019-2024)

Her collaborative work extends across international boundaries, with research partnerships visible through her academic network. As part of eSSENCE: The e-Science Collaboration, she contributes to advancing computational science methodologies with practical applications in environmental modeling and scientific computing.