SORS: freshwater salinization: a global challenge in need of data science solutions

All around the world humans are increasing the salt concentrations of freshwater ecosystems (rivers, lakes, wetlands, etc.) through a wide variety of activities such as agriculture or resource extraction. This phenomenon, known as freshwater salinization (FS), threatens aquatic ecosystems, limits socioeconomic activities, and impacts human wellbeing. Overall FS reduces biodiversity, alters river ecosystems and poses a risk to water availability, food security and human health. For a long time FS has been neglected by water managers and society at large, but recent ecological disasters (e.g. the Oder River disaster) have served as a wake up call. Now, more and more institutions (from the European Commission to the United Nations) are recognizing the important of addressing FS. However, we are still far from understanding how FS is caused and which regions of the world are at highest risk. This lack of knowledge limits effective management strategies and results in substantial socioeconomic losses. Within this context, artificial intelligence (AI), especially Deep Learning and advanced computing, can play a crucial role because they allow analyzing geospatial data at high spatial and temporal scales to unravel complex interactions between multiple factors (e.g. climate, anthropogenic drivers, etc.). In this talk I will present the challenge, the availability of data to address it and possible ways of moving forward by combining ecology with data science.Further, our research extended to investigating load balancing across processing cores, interconnection networks, and memory controllers. This exploration led to the development of a method capable of balancing the load on memory controllers, thereby reducing memory request round-trip latency.