Mula, Olga

"Partial Differential Equations (PDEs) are a powerful mathematical tool for describing the fundamental laws of nature. They play a crucial role in understanding countless phenomena, including key modern research areas such as the dynamics of machine learning algorithms or the behavior of quantum computers. However, solving PDEs exactly is often impossible, so we must rely on computer-based approximations. Ensuring these computations are both accurate and applicable to real-world problems is the core focus of my research.

Another important part of my research addresses how data can enhance the explanatory power of PDEs. I work on developing a unified mathematical and algorithmic framework that combines data with physical models in an optimal way. The ultimate goal is to create explainable, data-driven approximations that are more efficient - requiring less data than traditional black-box machine learning methods - while leveraging the deep insights provided by physics." 
More information: www.olgamula.com

• Numerical Analysis of Partial Differential Equations
• Mathematical Foundations of Scientific Machine Learning
• Data Assimilation and Inverse Problems
• High-Dimensional, Nonlinear Approximation
• Computational Optimal Transport
• Applications (e.g., haemodynamics, epidemiology, pollution, 3D-printing, nuclear physics)

• 2005-2011 Double Master's Degree in Applied Mathematics and Nuclear Engineering (École Polytechnique, France, and Escuela Politécnica, Madrid) 
• 2011-2014 PhD in Applied Mathematics (Sorbonne University) 
• 2014-2015 Postdoctoral Fellow (RWTH Aachen) 
• 2015-2022 Assistant Professor (Department of Mathematics, Paris Dauphine University, France) 
• 2022-2025 Associate Professor (Department of Mathematics, Eindhoven University of Technology) 
• since September 2025 Professorship of Computational Partial Differential Equations, Department of Mathematics, University of Vienna