Research
Current Research
My current work focuses on three main directions:
AI for geological characterization
Machine learning and computer vision for extracting geological and mineralogical information from drill-core images and subsurface data.
Machine-learning-assisted reactive transport
Surrogate models and computational methods for accelerating geochemical and reactive transport simulations.
Machine learning and optimization for sustainable materials
Combining physics-based modelling, machine learning, and mathematical optimization for the development of lower-carbon materials.
Research Projects:
Characterization of Rocks with Explainable and Uncertainty-Aware Machine Learning
View the project on the SNSF Data Portal →
Safe entrapment of As and Se in zeolites: Mechanistic understanding obtained from spectroscopic investigations and atomistic simulations
View the project on the SNSF Data Portal →
PRINCE: PRocesses at the INterface of iron and CEment
View the project on the SNSF Data Portal →
EURAD2: European Partnership on Radioactive Waste Management
SCENE: swiss center of excellence on net zero emissions:
PSI Career Return Program — Inverse Models for Particle Accelerators
The PSI Career Return Program supported my return to academic research and marked the beginning of my research activities at PSI.
About the PSI Career Return Program →
Collaboration
My research is inherently interdisciplinary. I collaborate with international researchers in e.g., geoscience, geochemistry, radioactive waste management, materials science, physics, chemistry, engineering, and applied mathematics.
I am particularly interested in research questions where mathematical and computational methods can be developed in close interaction with experimental data and domain expertise.
For publications related to these research areas, see my Publications page.