Matteo Convertino


My research is about the Understanding, Design, and possible Technology creation for Environmental, Health, and Energy Systems through Computational Science. Social and Economical factors are considered in the research as constraints or dynamical evolving factors in a Sustainability perspective.

Computational Sustainability is an interdisciplinary field that aims to apply techniques from computer science, information science, operations research, applied mathematics, and statistics for balancing environmental, economic, and societal needs for sustainable development. The main focus is on developing computational and mathematical models and methods for decision making concerning the management and allocation of resources in order to help solve some of the most challenging problems related to sustainability.

Computational sustainability problems are unique in scale, impact, complexity, and richness, often involving combinatorial decisions, in a highly dynamic and uncertain environment. The research pursued is inherently highly interdisciplinary, bringing together computer scientists, applied mathematicians, statisticians, biologists, environmental scientists, biological and environmental engineers, and economists.

I am strongly convinced about both the need to understand the unknown and uncertain factors of complex systems, and at the same time about the need to translate this knowledge into practice for the solution of real-world problem of complex systems. Thus my research is always at the crossroad of basic and applied research and it always integrate human and natural components of the systems analyzed.

My main research lines are:

  1. -Modeling complex ecosystems at large scales with metacommunity and metapopulation agent-based models, and niche models

  2. -Agent - based model for socio-technical systems

  3. - Risk and decision science models coupled to biophysical models (e.g. adaptive management and portfolio models)

  4. -Asset Management (in particular for biodiversity and infrastructure)

  5. -Decision scaling and multiscale effects on ecosystems

  6. -Resilience of Complex Systems

  7. -Models for environmental management and sustainability

  8. -Network-based model for design of sensor networks

  9. -Global sensitivity and uncertainty analysis of mathematical models

  10. -Image Analysis

  11. -Game-based models for quantification of stakeholder preferences and inference of stakeholder mental models and networks

  12. -Global trade network and supply chain network models for evaluation of policy options considering risk and decision scenarios (e.g. for health and food systems)

  13. -Stochastic multiple-network models for transport phenomena (e.g. epidemic diseases)

  14. -Probabilistic graphical model applications

  15. -System Metric Selection

  16. -Extreme events (e.g. cyclones and landslides) 

  17. -Aesthetic computing

  18. -Data mining and learning

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Complex Human Natural Systems