Scholar

Alexander Jacob

Google Scholar ID: VL6ZmeAAAAAJ

Vice-Head of Institute, Eurac Research, Institute for Earth Observation

Scalable ComputingData Driven ModellingMulti Sensor Data Fusion

Citations & Impact

All-time

Citations

674

H-index

i10-index

Publications

Co-authors

Contact

Resume

Academic Achievements

Received the best teacher award at the School of Architecture and the Built Environment in 2014. Invited multiple times as a Guest Lecturer to Karlstad University (Sweden) teaching courses on photogrammetry. Represents Italy in the Group on Earth Observation (GEO) for the Data & Knowledge working group and is one of the co-chairs of the Open Geospatial Consortium (OGC) GeoDataCubes Standards Working Group. Served as an advisor for the European Space Agency (ESA) and the Joint Research Centre (JRC) of the European Commission (EC) on Earth Observation Data Management and Computing.

Research Experience

Served as a research assistant (2003-2007) at the Institute of Geodesy and Institute of Physical Geodesy at TU Darmstadt, working on location-based services and software development for GNSS data management. Spent several months (2006) at Trimble Terrasat in Munich. Continued his experience in software development at Visual Meta GmbH in Berlin (2009), doing server-side development for search engines. Focused on remote sensing research at the Royal Institute of Technology. Joined Eurac Research at the Institute for Applied Remote Sensing in the technology for environment research group in 2015. Founded the Advanced Computing research group at the renamed Institute for Earth Observation in 2018. Currently serves as the Vice Head of the Institute for Earth Observation.

Education

Received a master of science and licentiate degree in Geoinformatics from the Royal Institute of Technology in Stockholm (Sweden) in 2012 and 2014. Prior to that, he studied engineering surveying at the Technical University of Darmstadt (Germany).

Background

Research interests include scalable computing, data-driven modeling, and multi-sensor data fusion. Focuses on efficient data organization and interoperable processing of EO data, covering topics from metadata and FAIR principles to data cubes, cloud & HPC computing, and scientific workflow description. Also focuses on explainable AI and uncertainty using EO data streams, integrating physics and data-driven modeling towards digital twins.

Miscellany