Matteo Convertino

Dr. Matteo Convertino, PhD PEng is Assistant Professor at the School of Public Health – Division of Environmental Health Sciences and Public Health Informatics Program at the University of Minnesota Twin-Cities. Dr. Convertino is a resident fellow of the Institute on the Environment, Institute for Engineering in Medicine, and of the Institute for Advanced Study at the University of Minnesota. Dr. Convertino is also Graduate Faculty in the Industrial and Systems Engineering Program and the Biomedical Informatics and Computational Biology Program. Dr. Convertino also holds an adjunct professor of Biodiversity Conservation and Ecosystem Health at the Institute of Forest Ecology, Environment and Protection, Chinese Academy of Forestry. Dr. Convertino holds a BSc, MSc, and PhD (’04,’06,’10) in Civil and Environmental Engineering from the University of Padova. Previous scientific positions were at Princeton University, University of Florida, and the Engineer Research and Development Center (ERDC) is the U.S. Army Corps of Engineers. His work is focused on the analysis, modeling, and management of complex natural-human systems for population health in a sustainability perspective. Dr. Convertino is the PI of the HumNat Lab composed by two PhD students, one postdoc and other research interns. The transdisciplinary mission of HumNat is to develop predictive complex systems multiscale models (physical-based and information-theoretical models) for biomedical, biological, behavioral, environmental and clinical research and related applications.Read more

These models can improve the diagnosis, etiognosis and prognosis of diseases and other ecosystem outcomes at the population scale. Morphological reaction-transport multicompartment models, global sensitivity and uncertainty models, and portfolio decision models based on information theory are the tools used and developed in HumNat research and practice. In particular Dr. Convertino is using the theory of self-organized criticality and mass-response function to investigate universalities and scale dependencies of patterns observed in nature, attribute patterns to process, and detect effective transmission networks (functional and structural) in a reverse engineering framework.

Current projects embrace a one-health approach – not only restricted to infectious diseases – and provide a technology / data analytics / theories for real time solutions of the problem investigated with a consequentialist perspective. The overall goal is an health-based design of socio-ecological system using engineering methods. Such issues are about food safety and defense, tubercolosis complex, leptospirosis/toxoplasmosis, cholera, antimicrobial resistance, surveillance networks, dynamic network biomarkers, natural and urban ecosystem health issues, anticarcinogen drug design, and science communication. In a broader perspective Dr. Convertino is involved in the promotion of complexity science and engineering methods into public health, smart and connected global system science, art-in-science, and systemic macro-epidemiology. Dr. Convertino teaches novel graduate classes (‘’Complex Systems Modeling for Population Health’’ and ”Environmental Health Engineering: Systemic Epidemiology and Design”) at the UofM School of Public Health and an undergraduate honor class (”How Nature Works”) in the UofM honor undergraduate program. Funding of Dr. Convertino are from the state of Minnesota (MnDRIVE initiative), University of Minnesota School of Public Health, Healthy Food and Healthy Lives Institute, 3M, USACE HQ, and NSF. The research is framed within national working groups of NIH (Interagency Modeling and Analysis Group – Multiscale Modeling Consortium, and the Office of Behavioral and Social Sciences Research) and National Academy of Engineering (Frontiers of Engineering program), and internationally (for instance in the FuturICT project, and the The World Health Organization Collaborating Centre for Complexity Science).

• 2015 CCS Chair – Complex Systems Epidemiology: Engineering Ecosystem Health