Research
My work sits at the intersection of mathematical optimization, machine learning, and partial differential equations — in particular, optimization methods with provable guarantees and their application to inverse problems and medical imaging.
Research Interests
- Mathematical optimization — first- and second-order methods, momentum, convergence theory
- Inverse problems — proximal methods, regularization, learned reconstruction
- Machine learning — optimization for learning, deep learning for scientific problems
- Partial differential equations and scientific computing
- Medical imaging
Journal Publications
Deep learning methods for inverse problems using connections between proximal operators and Hamilton–Jacobi equations
Momentum-based minimization of the Ginzburg–Landau functional on Euclidean spaces and graphs
Conference Publications
Methodological performance of data science in eco-sustainable design/engineering
CV
A full CV is available on request — please get in touch. Publications are also listed on Google Scholar.