Linus Wunderlich

Dr. Linus Wunderlich is a Lecturer in Financial Mathematics at Queen Mary University of London (QMUL), affiliated with the School of Mathematical Sciences. He holds a PhD in Hybrid Finite Element Methods for Non-linear and Non-smooth Problems in Solid Mechanics from Technical University Munich. His research focuses on integrating machine learning with numerical techniques in mathematical finance, including solving high-dimensional PDEs via neural networks and accelerating risk computations through Chebyshev interpolation. He also explores the theoretical foundations of AI and its applications in finance, engineering, and education.

Linus teaches courses such as Mathematical Tools for Asset Management and Masterclass in Business Analytics, emphasizing practical machine learning skills for business contexts. His work spans computational finance, numerical analysis, and interdisciplinary projects including Olympic medal prediction models and violin bridge modeling from CT scans.

Research grants include a £10,800 project with Numerical Algorithms Group Ltd on efficient volatility interpolation (2018–2019). He collaborates with institutions like LSE, Birkbeck, and University College Dublin, presenting at conferences such as SIAM Financial Mathematics and the German Probability & Statistics Days. His activities include developing interactive art projects and teaching machine learning courses during the pandemic.

No scientific awards are explicitly mentioned in the provided texts. His research interests and publications reflect a blend of mathematical rigor and applied innovation, bridging finance, engineering, and education.