The focus of my research in the Quantitative Biodiversity Dynamics (QBD) group is on understanding biodiversity change in response to ongoing climate/global change and anthropogenic activities. I am using spatio-temporal data like time series of remote sensing images and advanced machine/deep learning methods to characterise ecosystem stability and resilience and then test whether and to what extent they are linked to the pattern of biodiversity change. My main motivation has always been toward understanding the dynamics and functioning of nature and ecosystems through using advances in Geospatial data science and modelling tools (e.g., machine/deep learning). To address grand challenges that humanity faces (e.g., biodiversity loss, land cover/climate change), we need to take the recent explosive and remarkable progress in the availability of huge multiple sources of (big) spatial and temporal data (e.g., high-resolution satellite imaging, crowdsourcing, citizen science, drones, sensor networks) into account which together with enhanced methodological developments, can provide a great opportunity to answer some questions, e.g., why biodiversity changes are varying over space and time, how they are linked to climate change and other anthropogenic developments, how dynamics of ecosystems can provide insights/signals into their health status (and other risks to humans), how we can quantify the resilience of ecosystems, i.e., the capacity to maintain biodiversity, in a way to provide insights into biodiversity change and the risk of collapses (critical transitions) in species communities and ecosystem functions, and how such insights can contribute to reverse biodiversity loss. Such advances can also contribute towards successfully implementing strategic plans (e.g., CBD post-2020) and reaching sustainable goals and targets.