I lead an interdisciplinary research group in data science at WU Vienna University of Economics and Business. With a background in Computational Modelling (MSc) and Earth System Science (PhD), my main research interests are in developing new algorithms and software architectures to meet the scalability needs of planetary-scale geodata. Supported by an ERC Consolidator Grant, I lead the development of distributed high-performance systems that transform massive satellite datasets into actionable insights. A key technical contribution is the TWDTW algorithm, which I develop and maintain as an open-source software on CRAN. By engineering robust, scalable software, my team has enabled global-scale analysis of mineral extraction, revealing previously overlooked environmental footprints worldwide. I am committed to advancing algorithms and software that are robust and reliable to address the complexity of planetary-scale data and the long-term demands of global environmental monitoring.
A data-driven approach to mapping global commodity-specific mining land-use, Journal of Cleaner Production (2026)
An update on global mining land use, Scientific Data (2022)
A Time-Weighted Dynamic Time Warping Method for Land-Use and Land-Cover Mapping, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2016)