Environmental Geoinformatics: Employing AI (artificial intelligence) to unravel what previously was unachievable, it is now possible to fully exploit Earth Observation (EO) data for environmental monitoring and applications. The teams' interest is to bring the three multidiscipline (Geoinformatics, Climate, and AI) together, i.e., utilise AI as a powerful computing engine to manipulate EO remotely sensed data (Geoinformatics) and hydroclimate data (climate) to assess, evaluate and understand the impacts of climate extremes, climate variability and climate change on the environment. This includes but not limited to sensing of impacts of climate on stored freshwater (surface, underground, vegetation, and soil moisture) using satellites (GRACE/GRACE-FO, GNSS, Landsat, Sentinel-2, Precipitation and Altimetry, etc), reanalysis (ERA5, MERRA-2, etc) and hydroclimate models (GLDAS, WGHM, AWRA, etc). These satellites, reanalysis and hydroclimate models are employed to face the emerging challenges of the 21st century posed by increased food insecurity and extreme hydroclimatic conditions, e.g., severity and frequency of droughts in Australia and Greater Horn of Africa (GHA), and the changing monsoon characteristics in Asia and Africa leading to floods. Indeed, Africa, Asian and Australian continents are experiencing impacts of climate change that is affecting their water potential and food security, thereby worsening the situation for its inhabitants who rely heavily on rain-fed agriculture. Environmental Geoinformatics, thus enables a wide understanding of natural and human systems using “big data” that is both spatial and temporal in nature. In parallel, the team is engaged in Mathematical Geosciences: Hybrid-symbolic solutions that delivers hybrid symbolic-numeric computations (HSNC), is a large and growing area at the boundary of mathematics and computer science and currently an active area of research. The focus now is to employ AI to solve complex mathematical formulations in geosciences.