Our Laboratory focuses on research that drives technological,
environmental and social change. It includes advanced technologies
in the social aspect of environment management, embracing the
complexity of the human-environment relationship, and physical
model development for complex and non-trivial real-world problems in
the era of climate change. Our ultimate goal is to bridge the gap
between machine learning and geoscience for sustainability and
environmental management at the national and international (mainly
in the Mediterranean) scales. We understand that machine learning, in
general, and deep learning, in particular, offer promising tools to build
new data-driven models for Earth system components and thus build
our understanding of ecosystems. Yet, accepting that data-driven
machine learning approaches in geoscientific research cannot replace
physical modelling but strongly complement and enrich it. Our primary
scientific interests are developing hybrid approaches, coupling
physical processes (physical laws and physics-domain-specific
knowledge) with the versatility of data-driven machine learning, also
known as physics-aware machine learning, to better understand the
ecosystems, biodiversity, dynamic processes and environmental
responses to stressors, and emphasizing sustainability and decision
support system development aligned with the UN Sustainable
Development Goals (SDGs).
