🔬Machine Learning Engineer & AI Researcher at DFKI
I am a Machine Learning Engineer and AI Researcher at DFKI in Kaiserslautern, working on multimodal ML, privacy auditing, and production-oriented ML systems.
My current work covers multimodal crop-yield prediction using satellite imagery, weather data, and geospatial context; privacy auditing for ML models and LLMs, including membership inference and model inversion risk; and MLOps infrastructure for repeatable training, evaluation, and deployment.
The work has led to publications in remote sensing and machine learning venues, along with deployed pipelines used with partners in Germany.
My path here was non-linear. I started with electronics engineering at HBTI Kanpur, then spent four years at IBM building distributed Java EE systems for enterprise telecom before moving into AI through an M.Sc. in Cognitive Science at Osnabrück University. That engineering background still shapes how I think about ML systems.
Multimodal crop-yield prediction
Field and sub-field scale yield forecasting using Sentinel-2 imagery, weather data, soil information, and harvester GPS records. The work combines modeling with data and training pipelines built for real farming workflows, and contributed to publications in remote sensing and machine learning venues.
Privacy auditing and trustworthy AI
Practical privacy risk assessment for ML models and LLMs, covering membership inference, model inversion, and compliance workflows under EU AI Act requirements for healthcare applications. I treat privacy analysis as part of the engineering workflow, with testing, reporting, and decision support that can be repeated.
Production ML systems
Turning research code into reliable, reproducible infrastructure with Kedro pipelines, MLflow experiment tracking, Apache Airflow orchestration, Docker, and CI/CD. This work supports the transition from experiments to systems that can be maintained and used outside notebook-based research.
Recent papers connected to my work in remote sensing, multimodal learning, and trustworthy AI.