Matteo Convertino

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I Love… Nature, Water, Biology, Aquatic Ecosystems, Beaches, Patterns, Design, Art, Philosophy, Sport: Everything that Flows!

Ask me about… Complex Networks, Information, Entropy, Fractals, Waves, Biodiversity, Eco/Biodesign … Food and Style!

 

Short CV 

Associate Professor
Nexus Group, Director
Division of Media and Network Technologies
Graduate School of Information Science and Technology
Gi-CoRE Station for Big Data & Cybersecurity, Frontier Science Unit Department of Electronics and Information Engineering
Hokkaido University, Sapporo, JP
http://www.ist.hokudai.ac.jp/eng/

Phone: +1 781-645-6070 , Skype: convertino.matteo; email: matteo@ist.hokudai.ac.jp / complexity@icn.ist.hokudai.ac.jp

web: https://nexuslabblog.wordpress.com/ , https://github.com/matteoconvertino

Linkedin   Twitter

Personal
Born on July 20, 1982; Italian and USA Citizenship, JP permanent residency (scientific merit); Married, 1 children

Education

Degrees

Ph.D. Civil and Environmental Engineering Sciences (Theoretical and Computational Biocomplexity), summa cum laude, University of Padova (UniPd), April 2010. Dissertation title: Patterns in Ecology and Geomorphology of River Basin Ecosystems, advisers: A. Rinaldo and I. Rodriguez-Iturbe (Princeton University)

M.Sc. Civil and Environmental Engineering (Fluid Mechanics and Hydrology), University of Padova, September 2006. Thesis title: Analysis and Modeling of the Ontogeny of Optimal Channel Networks with Heterogeneous Rainfall, advisers: A. Rinaldo, R. Rigon (University of Trento), and A. Maritan (UniPd)

B.Sc. Civil and Environmental Engineering (Environmental and Structural Engineering), University of Padova, July 2004 Thesis title: FEM Modeling and Experimental Characterization of Innovative and Environmentally Sustainable Materials for Pavement Infrastructures, adviser: M. Pasetto

Diploma (High School science major), G.B. Belzoni High School, Padova, Italy, 2001 Projects titles: Structural and Functional Road Project in a Mountain Area in Northern Italy and associated Hydrological Management, comprehensive mark 100/100 with honors

Publications

Google Scholar profile 

Bio

Research Vision

PREVIOUS RESEARCH. My previous research has been articulated in the following areas.

Theoretical and Computational Ecology, and Ecodesign. Neutral metacommunity [3,6] and niche metapopulation models [9,10,11,29] were used to investigate the role of scale-free river networks [1,7,8] and other network topologies into the organization of diversity over space and time [2,4]. Combined niche-neutral models were also used to predict patterns of diversity and species distribution as a function of climate change and management strategies in order to find universal drivers and controls. Analytical studies were also carried out to study biotic and abiotic aggregate formations from bacteria to large mammals; different analytics were linked to each other and a general double Pareto distribution of aggregate size was formulated [1]. Bacteria pattern emergence (in particular E. coli) was studied using a mechanistic agent-based model where nutrient concentration was found to be responsible for the striking multistate transition of bacteria dynamics/patterns with different stability states. These studies on bacteria network-like pattern formation also lead to the engineering of Bio-Inspired Patterned Sensors [5] with a variety of different applications.

Environmental Dynamics, Ecohydrogeomorphological Modeling, and Extremes. Initial worsk were concentrated on the hydrogeomorphological evolution of ecosystem networks and ecohydrological dynamics as a driver of diversity [3,6,7]. Later on, studies about floods [9], cyclones [11] and landslides [8] were performed for better defining extreme statistical patterns, predictive MaxEnt models and optimal ecosystem management [4,24]. The basis of my research is grounded on the hydrodynamic foundation of my knowledge, statistical physics mentorship, and the study (and experience through my childhood) about high waters in Venice [9]. These studies highlighted the enormous importance of environmental dynamics considering a variety of animal and human population patterns [14] and created models were environmental dynamics is taken into account explicitly.

Ecosystem Health and Disease Forecasts. Ecosystem health was broadly defined as the linked health of animal and human populations considering both acute and chronic health outcomes [14]. Early work was done in the context of waterborne infectious diseases such as cholera [17], and later on about water-based diseases such as dengue, yellow fever and leptospirosis [15]. These ongoing works are in collaboration with WHO/PAHO [15,16,18] and CDC [12] that are interested in developing a predictive outbreak infrastructure and a dynamic risk assessment model. Later work, sponsored by CDC and the White House was focused on influenza as a typical socially transmissible disease with the purpose to develop a computational epidemic infrastructure [20]. On the basis of these works the connection between infectious pathogens and humans was investigated considering the food connection and how food supply chain can be designed considering also health outcomes [18]. Lastly, in a broader ecosystem health definition, species invasion were studied as a function of ecological competition and ecosystem structure [19]; current studies about Aquatic Invasive Species in MN are ongoing and embrace citizen science initiatives via App-based reporting of species invasion and other technology such as drones [16].

Risk and Decision Sciences. Risk and decision models [21] were developed integrated to environmental dynamic models such as the Enhanced Adaptive Management model for the Greater Everglades Area [22]. These models consider all potential sequential decisions leading to alternative states of ecosystems in terms of structure and function with particular focus on key species, diversity and ecosystem services related to human populations. Recently these models have been linked to niche base model for predicting water-based infectious diseases and a linkage between (manageable) drainage density and ID incidence was found. Mental modeling was also used to incorporate stakeholder preferences into models [24]. A portfolio decision model was developed to deal with systemic risk and to define optimal system states [25]. Systemic indicators that consider ecosystem’s complexity are designed via value-based decision models [23] that are better suited to map systemic risk and resilience [21].

Model Design and Evaluation, Network Inference, and Data Visualization. Research about model design and evaluation has been based on all discipline-specific applications of my research. In particular information theoretic global sensitivity and uncertainty analyses [13,28-30] was developed to evaluate models and data with respect to a predicted outcome or multicriteria indicators [23]. Novel network-based visualization [27,28] were also realized considering these uncertainty propagation model results to define drivers of ‘’endemic-’’ and ‘’epidemic-like’’ processes. More recently data-based inferential models of functional and structural networks were developed considering non-linear information quantities such as transfer entropy and a novel information balance equation for defining importance and interactions of factors [27]. Node/variable dynamics and pairwise interactions are assessed via these information theoretic models.

 

PROPOSED RESEARCH. My proposed research at is articulated in the following areas.

Collective Behavior and Bio-inspired Design. A unified model of criticality, considering spontaneous self-organization and value-based decisions, is my main aspiration for understanding and predicting ecological and evolutionary patterns in populations. Criticality has been explored as a separate theory in sociology and physics but with many common elements that can be highly useful in ecological research and management. Criticality, under neutral or non-neutral conditions, is about organized connections and this particular topic is active considering my team research, in collaboration with the University of Technology Sydney, to understand optimal bio-inspired decision-making models for engineering autonomous swarm drones. The research, connected to my previous bacteria studies, has also the power to explore energetic questions in the functioning of organisms by engineering flying motifs and observing their performance under different noise types and missions. Engineering collective behavior in silico (in computer models or in drones) makes possible to reserve-engineer the knowledge gained by experiments to understand much better information flow and self-organization under noise in animal populations. Of course this can improve collective drone or other robots missions that also have ecological applications. Other research proposed about collective behavior is ongoing considering the collective dynamics of hibernating biomarkers (‘’dynamical biomarker networks’’) in animal populations, such as bears. This research that is in partnership with colleagues at Medtronic and the University of Minnesota, has the great promise to develop network-based understanding of connected compounds that hibernate organs with great applications in medicine. Similar research is ongoing about the species collective dynamics of the microbiome for different diseases as well as for the food-microbiome nexus considering worldwide data for engineering diversity-controlled microbial mixtures (such as for optimal fecal transplants) and understanding diet-based population outcomes as well as epigenetic mechanisms in humans.

Ecosystem Health – Population Nexus (Envirome). The ecosystem-population health nexus is addressed considering both disease-specific classes (such as vectorborne diseases) and pathologies as well as biological markers indicating critical changes in ecosystems. This research has the purpose to identify ecosystem drivers of acute, chronic and hereditary diseases as well as improving epidemiological knowledge, population health sciences and personalized medicine. The research will be focused on three topics: (i) climate-sensitive infectious disease analysis and forecasting with focus on water-based diseases in subtropical/tropical South-East Asia; (ii) microbiome ontogeny and epigenetics, and design of potential personalized medicine treatments; and (iii) dynamic biomarker network dynamics for discovery of optimal biological function and design of biotechnology. All these studies will contribute to this new figure of ‘’ecosystem pathologist’’ that ideally is a quantitative skilled individual able to perform ecosystem diagnosis, etiognosis and prognosis; a sort of ‘’health engineer’’ with the ability to identify ecosystem dynamical drivers of diseases in populations and individuals. All topics are certainly intersecting each other in a big population connectome framework (see Fig. 1); for instance the ecosystem microbiome (the ‘’language of nature’’) is hypothesized to control the spillover of ‘’bad’’ pathogens from the environment to animals and humans. Methodologically speaking, a common information spreading model is aimed to be developed for slow and fast processes able to identify timing, magnitude, and networked drivers of diseases.

connectome.png

Figure 1. Population connectome. The nexus between biological markers and environmental dynamical factors help to understand and predict ecological and evolutionary patterns in populations, such as diseases. By using network and information theoretic models it is possible to untangle this population complexity and engineering technology or decisions aimed to optimize desired population outcomes.

 

Many ecosystem-disease processes have not been clearly investigated and tools like network and information theory are going to be able to contribute to new discoveries. The research related to infectious diseases will seek to understand large-scale social and environmental teleconnections among areas and countries in SE Asia, as well common environmental niche determinants. This will be useful to predict disease incidence as well as diversity (syndemics). Both tasks are of great utility to colleagues at WHO for the allocation of resources considering syndemics experienced by the affected countries and the resources available. Environmental hydrology-based controls will be sought versus a posteriori medical treatments that do not address the roots of diseases. Preliminary studies are ongoing to apply the same models to better understand the collapse of ancient civilizations as a function of erroneous ecohydrological management of ecosystems. This will improve our understanding of the socio-ecological system nexus. Lastly, in collaboration with data science colleagues working jointly with me via the NIH Big Data Science initiative and industries (3M and Medtronic) I will continue my research about dynamical biomarker networks of symptoms (e.g. pain in sickle cell disease patients for engineering automated biomarker-targeted drug delivery via model on sensors) and biological phenomena (e.g. metabolic activity for engineering organ hibernation inspired by hibernating dynamics of animals).

Eco-hydrodynamics, Ecosystem Diversity, Valuation and Optimal Eco Design. Research about biodiversity is mainly focused on subtropical and tropical terrestrial and oceanic ecosystems considering the multiplex interaction of bird, fish, and plant species. A Sakigake proposal was submitted in May 2018 for assessing multitrophic biodiversity in Japan as a function of natural and anthropogenic drivers. This study also proposed novel maximum entropy models detecting environmental niches, information transfer models for inferring species interaction networks, stochastic neutral metacommunity model models for predicting biodiversity, and portfolio decision model for the selection of optimal biodiversity management plans. The health of an ecosystem, determined more by a steady stable growth of diversity rather than the average value of species diversity, is strongly affecting animal and human populations considering the multitrophic food web dependencies. This is particularly true for human populations, e.g. in Japan that make an almost direct consumption of plant and fish species whose existence and health is critically important. The interest in coastal and oceanic ecosystems is related to my origin (Venice) and previous work in Florida about coastal system diversity, community and population fish and bird risk related to climate change, and my background in hydrodynamics. A project with Dutch and Japanese collaborators at JAMSTEC is starting in the Ryukyu archipelago to understand the multiscale linkage between hydrodynamic criticality (determined by natural oceanic cycles and human-induced changes also related to navigation) and coral ecosystem criticality looking into species diversity and coral ‘’symptoms’’ such as bleaching. This study is quite interesting because multiple streams of data at different scales will be considered, from microbial small scale data, environmental data such as hydrodynamic and climate data, to biodiversity data of coral species. Coral ecosystems can constitute a great analogy to human ecosystems where both biology and environmental dynamics are highly coupled to each other in determining the fate and evolution of species in short and long term. Technology driven ecosystem monitoring and management will be sought, for instance via the use of unconventional technology such as sound-recorders and Apps. Like for health outcomes I proposed a needed portfolio-based ecosystem valuation for determining critical areas to preserve and manage considering actors’ needs and resources.

Complex Network Inference, Multiscale Predictions & Systemic Risk/Decisions. This theme is purely related to methods for inferring complex system dynamics such as network inference, node and link dynamical characterization, cluster detection, structure-function duality, space-time nexus, identification of universality and singularities and network-based predictions of complex systems. I believe all these topics are highly relevant to big- and small-data problems where very little is known about detailed processes underlying observed patterns as well as for all problems where rapid and extremely accurate processing of data is necessary such as in medicine. Using mostly biological data I am working for identifying dynamic behaviors of systems considering their probabilistic behavior over space and time and information flow among interdependent variables. Uncertainty propagation plots are used to characterize complex system dynamics and identifying cluster singularities. Hyperbolic and quantum networks are explored for highly non-linear systems.

Ecology and Evolution of Human Population Patterns. I am highly interested in defining a unified theory of criticality for biological and social systems and both systems can be used to inspire the understanding of each other and generate a common general knowledge of how they work independently of the fine scale ingredients. As much as biology has its own language (e.g. related to genetic signals), the ecology and evolution of spoken and written languages in populations, or of any other communication form such as art, reveals the social processes underlying human dynamics. In connection with linguists faculty at Kyoto University I am starting to untangle the complexity of Japanese language over time and its commonalities and peculiarities with respect to other languages and as a function of socio-environmental conditions. The research has also practical importance for improving speech recognition systems beyond the pure anthropological interests. Other social phenomena are of my interest such as fashion and scientific production evolution. Complexity in art is something that fascinates me a lot for educational and creativity purposes such as network 3D printing and data sonification.

REFERRED WORK.

Theoretical and Computational Ecology.

[1] Convertino, M., Simini, F., Catani, F., Linkov, I., Kiker, G.A. (2013). Power-law of Aggregate-size Spectra in Natural Systems, EAI Endorsed Transactions on Complex Systems, http://eudl.eu/doi/10.4108/trans.cs.1.2.e2

[2] Convertino, M., R. Mangoubi, I. Linkov, N.C. Lowry, M. Desai (2012). Inferring Species-Richness and Species-turnover by Statistical Multiresolution Texture Analysis of Satellite Imagery, http://dx.plos.org/10.1371/journal.pone.0046616, PLoS ONE

[3] Convertino, M., R. Muneepeerakul, S. Azaele, E. Bertuzzo, A. Rinaldo, and I. Rodriguez-Iturbe (2009). On neutral metacommunity patterns of river basins at different scales of aggregation, 45, W08424, 10.1029/2009WR007799, Water Resources Research

[4] Convertino M., R. Muñoz-Carpena, G.A. Kiker, S.G. Perz Design of optimal ecosystem monitoring networks: Hotspot detection and biodiversity patterns, Stochastic Environmental Research and Risk Assessment, 29 (2015), pp. 1085-1101

[5] McLamore E.S., Convertino M., Hondred J, Das S., Claussen J.C. et al. (2016) Bio-inspired patterned networks (BIPS) for development of wearable/disposable biosensors, Proc. SPIE 9863, Smart Biomedical and Physiological Sensor Technology

[6] Convertino, M. (2011). Neutral Metacommunity Clustering and SAR: River Basin vs 2-D Landscape Biodiversity Patterns, 222, 11, 1863-1879, Ecological Modelling

 

Environmental Dynamics, Ecohydrogeomorphological Modeling, and Extremes.

[7] Convertino, M., R. Rigon, A. Maritan, I. Rodriguez-Iturbe, and A. Rinaldo (2007). The probabilistic structure of the distance between tributaries of given size in river network, 2007WR006176, Water Resources Research

[8] Convertino, M., F, Troccoli, Catani, F. (2013). Detecting fingerprints of landslide drivers: a maxent model, http://onlinelibrary.wiley.com/doi/10.1002/jgrf.20099/abstract, Journal of Geophysical Research – Earth Surface

[9] Convertino, M., Nardi F., Kiker, G., Muñoz-Carpena R., Troccoli A., Linkov I., (2011). Epitomes of a bottom- up hydro-geo-climatological analysis and modeling to face sea-level rise in coastal ecosystems, Water Encyclopedia, ”Climate Sustainability: Understanding and Addressing Threats to Essential Resources”, Elsevier book (”Water Encyclopedia” section). Editor: R.A. Pielke Sr.

[10] Convertino, M., M.L. Chu-Agor, R.A. Fischer, G. Kiker, R. Muñoz-Carpena, I. Linkov (2012). Shorebird Patch Dynamics as Fingerprint of Coastline Variation due to Climate Change, in the special issue ”Wetlands in a Complex World”, DOI:10.1186/2192-1709-1-9, Ecological Processes

[11] Convertino, M., J. Elsner, G. Kiker, R. Muñoz-Carpena, Martinez, C.J., R. Fischer, I. Linkov (2011). Do Tropical Cyclones Shape Shorebird Patterns? Biogeoclimatology of Snowy Plovers in Florida, 10.1371/journal.pone.0015683, PLoS ONE

 

Ecosystem Health and Disease Forecasts.

[12] Liu Y, Hoppe B, and Convertino M, (2018) Threshold Evaluation of Emergency Risk Communication for Health Risks Related to Hazardous Ambient Temperature, Risk Analysis

[13] Convertino M, TR Church, GW Olsen, Y Liu, E Doyle, CR Elcombe, et al., Stochastic Pharmacokinetic-Pharmacodynamic Modeling for Assessing the Systemic Health Risk of Perfluorooctanoate (PFOA), (2018) Toxicological Sciences

[14] Travis D., Convertino M., Shaffer C., Gillespie T., Alpern J., Stauffer W., Robertson C., Kennedy S., Craft M., (2017), Biodiversity and Health, Engineering Ecosystem Heath via Science and Technology, in ‘’One Health”, Editors: Herrmann JA and Johnson-Walker YJ, John Wiley Sons

[15] Convertino M, Y. Liu, H. Hwang, Optimal surveillance network design: a value of information model, Complex Adaptive Systems Modeling, 2 (1) (2014)

[16] Reis S, E. Seto, A. Northcross, N.W.T. Quinn, M. Convertino, R.L. Jones, H.R. Maier, U. Schlink, S. Steinle, M. Vieno, M.C. Wimberly, Integrating modelling and smart sensors for environmental and human health, Environ. Model. Softw., 74 (2015), pp. 238-246

[17] You YA, Ali M, Kanungo S, Sah B, Manna B, Puri M, Convertino M et al. (2013) Risk Map of Cholera Infection for Vaccine Deployment: The Eastern Kolkata Case. PLoS ONE 8(8): e71173. https://doi.org/10.1371/journal.pone.0071173

[18] Convertino, M., Liang, S.: Probabilistic supply chain risk model for food safety. GRF Davos Planet@Risk 2 (2014)

[19] Lagerwall, G., G. Kiker, R. Muñoz-Carpena, M. Convertino, A. James, N. Wang (2012). A Spatially- Distributed, Deterministic Approach to Modeling Typha domingensis (Cattail) in an Everglades Water- controlled Wetland, in the special issue ”Wetlands in a Complex World”, DOI: 10.1186/2192-1709-1-10, Ecological Processes

[20] McGowan C., … Convertino M…, et al., (2019) Collaborative efforts to forecast seasonal influenza in the United States, 2015–2016, Scientific Reports, volume 9, 683

 

Risk and Decision Sciences.

[21] Park J, Seager TP, Rao PS, Convertino M, Linkov I (2012) Integrating risk and resilience approaches to catastrophe management in engineering systems. Risk Anal 33(3):356–367

[22] Convertino, M., C.M. Foran, J.M. Keisler, L. Scarlett, A. LoSchiavo, G.A. Kiker, I. Linkov (2013). Enhanced Adaptive Management: Integrating Decision Analysis, Scenario Analysis and Environmental Modeling for the Everglades, Scientific Reports (Nature Publishing Group), 3, 2922, doi:10.1038/srep02922

[23] Convertino, M., K. Baker, C. Lu, J.T. Vogel, B. Suedel, I. Linkov (2012). Use of Multi-Criteria Decision Analysis to Guide Metrics Selection for Ecosystem Restoration, [free model case study available at PANGAEA – Data Publisher for Earth & Environmental Science, http://doi.pangaea.de/10.1594/PANGAEA.776746%5D Ecological Indicators

[24] Convertino M, Murcia C, Munoz-Carpena R, (2014) ”Reading the Minds for Quantitative Sustainability: Assessing Stakeholder Mental Models via Probabilistic Text Analysis, BOOK: ”Information, Models, and Sustainability: Policy Informatics in the Age of Big Data and Open Government”, Springer

[25] Convertino M, Valverde LJ Jr (2013) Portfolio Decision Analysis Framework for Value-Focused Ecosystem Management. PLoS ONE 8(6): e65056. https://doi.org/10.1371/journal.pone.0065056

[26] Convertino M, Convertino N, (2014) Sequential portfolio decision model for epilepsy death risk reduction International Congress on Environmental Modelling and Software, iEMSs 2014 – San Diego

 

Model Design and Evaluation, Network Inference, and Data Visualization.

[27] Servadio J, M Convertino, (2018) Optimal information networks: Application for data-driven integrated health in populations, Science Advances 4 (2), e1701088

[28] Convertino, M., R. Muñoz-Carpena, G. Kiker, M.L. Chu-Agor, R. Fischer, I. Linkov (2012). Epistemic Uncertainty in Predicting Shorebird Biogeography Affected by Sea-Level Rise, http://dx.doi.org/10.1016/j.ecolmodel.2012.04.012, Ecological Modelling

[29] Aiello-Lammens, M., Chu-Agor, M.L., Convertino, M., Fischer, R.A., Linkov, I., Akcakaya, H.R., (2011). The impact of sea-level rise on Snowy Plovers in Florida: Integrated Geophysical, Habitat, and Metapopulation Models, DOI: 10.1111/j.1365-2486.2011.02497.x, Global Change Biology

[30] Convertino, M., M.L. Chu-Agor, I. Linkov, G.A. Kiker, R. Muñoz-Carpena (2013). Untangling drivers of species distributions: Global sensitivity and uncertainty analyses of MaxEnt, Environmental Modeling and Software, 51, 296Ð309, http://www.sciencedirect.com/science/article/pii/S1364815213002272

 

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