Deep generative models and explainable machine learning are two emerging areas of data science that we can use to address current challenges in agricultural and environmental sciences. Deep generative models are neural networks that are capable of learning complex data distributions. In general, they can be used for a variety of applications, such as anomaly detection or prediction. Explainable machine learning, which analyzes the decision-making process of machine learning methods in more detail, is used whenever an explanation for the result is required in addition to the result. This can be done for various reasons, e.g. to increase confidence in the outcome or to derive new scientific knowledge that can be inferred from patterns in the decision process of the machine learning model. This talk addresses methods from both areas and provides examples of their application in agricultural and environmental sciences.

Prof. Ribana Roscher received her Dipl.-Ing. and Ph.D. degrees in Geodesy from the University of Bonn, Germany, in 2008 and 2012, respectively. Until 2022, she was a Junior Professor of Remote Sensing at the University of Bonn. Prior to this, she was a Postdoctoral Researcher at the University of Bonn, the Julius-Kühn Institute, Siebeldingen, Germany, Freie Universität Berlin, Germany, and the Humboldt Innovation, Berlin, Germany.

In 2015, she was a Visiting Researcher at the Fields Institute, Toronto, Canada, and in 2018 she was a Visiting Researcher at UCLA Institute for Pure & Applied Mathematics, Los Angeles, USA.

Since 2022, Ribana Roscher is a Professor of Data Science for Crop Systems at the University of Bonn, Germany, and heads the same-titled group at IBG-2, Forschungszentrum Jülich, Germany. Her research focus is pattern recognition and machine learning specifically for applications in agricultural and environmental sciences.

 
About the EuroCC-2 project

EuroCC 2 will work to identify and address the skills gaps in the European High Performance Computing (HPC) ecosystem and coordinate cooperation across Europe to ensure a consistent skills base.

The role of EuroCC 2 is to establish and run a network of more than 30 NCCs across the EuroHPC Participating States. The NCCs act as single points of access in each country between stakeholders and national and EuroHPC systems. They operate on a regional and national level to liaise with local communities, in particular SMEs, map HPC competencies and facilitate access to European HPC resources for users from the private and public sector.

EuroCC 2 delivers training, interacts with industry, develops competence mapping and communication materials and activities, and supports the adoption of HPC services in other related fields, such as quantum computing, artificial intelligence (AI), high performance data analytics (HPDA) to expand the HPC user base.

This project has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 101101903. The JU receives support from the European Union’s programme Digital Europe and Germany, Bulgaria, Austria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, Greece, Hungary, Ireland, Italy, Lithuania, Latvia, Poland, Portugal, Romania, Slovenia, Spain, Sweden, France, Netherlands, Belgium, Luxembourg , Slovakia, Norway, Switzerland, Turkey, the Republic of North Macedonia, Iceland, Montenegro and Serbia.