Research Focus
I am a scientist at the Karlsruhe Institute of Technology (KIT), working at the intersection of artificial intelligence and Earth system science. I also serve as Scientific Coordinator for the ECMWF Machine Learning Training Program under the DestinE initiative, contributing to international efforts in AI-driven Earth system modeling.
My research focuses on developing physically consistent and interpretable AI models to better understand atmospheric processes across scales. In particular, I work on representation learning and generative modeling using satellite observations and km-scale numerical simulations.
More broadly, my goal is to bridge the gap between predictive skill and scientific understanding, enabling trustworthy AI for weather forecasting and climate-scale applications.
News & Updates
- ECMWF–ESA Workshop — Presented work on evaluating km-scale numerical models against satellite observations.
Slides · Recorded Talk - April 2026 — Appointed to a permanent Machine Learning Scientist position at the Swedish Meteorological and Hydrological Institute (SMHI), starting September 2026.
- September 2025 — Scientific Coordinator for the ECMWF Machine Learning Training Program under the DestinE initiative.
Featured Research
Ongoing Work
Evaluating convection in km-scale numerical models against satellite observations through a shared latent representation.
Latent-space evolution of cloud systems from satellite observations and numerical simulations (ICON, IFS).
Recent Publication
Learning representations of geostationary satellite imagery to identify low-level cloud systems and quantify their impact on solar energy variability.
Framework linking cloud organization to solar energy ramp events.
Current Work
- Evaluating km-scale numerical models in latent representation space
- Developing conditional diffusion models for satellite–radar mapping
- Studying cloud regime transitions and multiscale dynamics
Research Themes
Representation Learning for Cloud systems
Learning compact representations of satellite observations to study cloud regimes, transitions, maturity, and diurnal variability.
AI for Physical Understanding
Using deep learning not only for prediction, but also as a tool to uncover structure in complex atmospheric systems.
Generative Models for Earth Systems
Developing probabilistic and generative models to connect satellite observations, radar measurements, and physical processes.