I am Assistant Professor of Data Science at Utica College (UC) in Utica, NY, where I teach within the 100% online Master of Science in Data Science program. I earned my PhD in Geographic Information Science at the Department of Geography and Earth Sciences (GES) of the University of North Carolina at Charlotte (UNCC), while being a member of the Center for Applied Geographic Information Science (CAGIS). I have received my Bachelor of Science from the University of Zurich, Switzerland and Master of Arts from UNCC.

I am interested in cyberinfrastructure and big data analytics to understand the computational aspects of modelling complex spatiotemporal phenomena. More specifically, I explore and develop methods to accelerate point pattern analysis to tackle questions within a wide range of scientific domains, such as spatial epidemiology, landscape ecology, and physical geography.

Previously, I worked at the National Cooperative for the Disposal of Radioactive Waste in Switzerland (NAGRA), and held internships at the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), as well as at the Geographic Information Science and Technology group (GIST) at Oak Ridge National Laboratory (ORNL).

I  have published my work in peer-reviewed journals, such as the International Journal of Geographical Information Science (IJGIS), Spatial and Spatiotemporal Epidemiology (SSTE), and the ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences.

As I am also interested in data mining, machine learning and database technologies, and my enthusiasm for such approaches has evolved from “tools to help my research” to an actual interest in it’s own right. Therefore, I have pursued and earned the Graduate Certificate in Advanced Databases and Knowledge Discovery at the Department of Computer Science at UNCC.

I’m passionate about sports, especially the beautiful Olympic discipline of table tennis, which I have been playing competitively for more than 25 years. I maintain a USATT rating of 2200 and higher.