I am an Associate Professor of Data Analytics at Trinity Business School, Trinity College Dublin, The University of Dublin and a Senior Fellow of the Higher Education Academy. I was the Director of Postgraduate Teaching at the Department of Management Science, Lancaster University Management School overseeing MSc programmes in Business Analytics, Management Science and Marketing Analytics, Logistics and Supply Chain Management, e-Business and Innovation, and Project Management.

My research interests lie in the areas of predictive analytics using simulation. I am particularly interested in simulation modelling methodology (symbiotic simulation, hybrid modelling, agent-based simulation, discrete-event simulation) with applications in operations and supply chain management (e.g. hospital, manufacturing, transportation, warehouse) and social dynamics (e.g. diffusion of perception). Currently, I am the associate editor of the Journal of Simulation and the secretary of The OR Society‘s Special Interest Group in Simulation. I am the track coordinator of Agent-Based Simulation for the Winter Simulation Conference 2018.

My research interests include policy informatics and decision making, modeling in policy analysis and management decisions, public health management and policy, and the role of public value in policy development. I am particularly interested in less mainstream approaches to modeling that account for learning, feedback, and other systems dynamics. I include Bayesian inference, agent-based models, and behavioral assumptions in both my research and teaching.
In my dissertation research, I conceptualize state Medicaid programs as complex adaptive systems characterized by diverse actors, behaviors, relationships, and objectives. These systems reproduce themselves through both strategic and emergent mechanisms of program management. I focus on the mechanism by which citizens are sorted into or out of the system: program enrollment. Using Bayesian regression and agent-based models, I explore the role of administrative practices (such as presumptive eligibility and longer continuous eligibility periods) in increasing enrollment of eligible citizens into Medicaid programs.

As a Master’s Thesis student, I am intended to apply Artificial Intelligence to an already existing model with the aim of making it more accurate.

Even though I do not have the focus point and the scope of the research clear yet, the road map is set to start from a very simple model to validate the technology and methodology used and then continue with more abitiuos projects.

I like the co-operation that I have found in this space and I think that I could both learn a lot from the community and add value with my novel trials and findings.

Of course I would be pleased to update the status of my project and I would try to help if I have the proper knowledge or different angle to other peers who seek for seconds opinions.

Thank you,
Francisco

The particular focus of the research is the development of a spatial and Agent-Based Model to capture the motivations underlying the decision making of the various actors towards the investments in the quality of land and institutions, or other aspects of land use change. Inductive models (GIS and statistical based) can extrapolate existing land use patterns in time but cannot include actors decisions, learning and responses to new phenomena, e.g. new crops or soil conservation techniques. Therefore, more deductive (‘theory-driven’) approaches need to be used to complement the inductive (‘data-driven’) methods for a full grip on transition processes. Agent-Based Modeling is suitable for this work, in view of the number and types of actors (farmer, sedentary and transhumant herders, gender, ethnicity, wealth, local and supra-local) involved in land use and management. NetLogo framework could be use to facilitate modeling because it portray some desirable characteristics (agent based and spatially explicit). The model develop should provide social and anthropological insights in how farmers work and learn.

Social network analysis has an especially long tradition in the social science. In recent years, a dramatically increased visibility of SNA, however, is owed to statistical physicists. Among many, Barabasi-Albert model (BA model) has attracted particular attention because of its mathematical properties (i.e., obeying power-law distribution) and its appearance in a diverse range of social phenomena. BA model assumes that nodes with more links (i.e., “popular nodes”) are more likely to be connected when new nodes entered a system. However, significant deviations from BA model have been reported in many social networks. Although numerous variants of BA model are developed, they still share the key assumption that nodes with more links were more likely to be connected. I think this line of research is problematic since it assumes all nodes possess the same preference and overlooks the potential impacts of agent heterogeneity on network formation. When joining a real social network, people are not only driven by instrumental calculation of connecting with the popular, but also motivated by intrinsic affection of joining the like. The impact of this mixed preferential attachment is particularly consequential on formation of social networks. I propose an integrative agent-based model of heterogeneous attachment encompassing both instrumental calculation and intrinsic similarity. Particularly, it emphasizes the way in which agent heterogeneity affects social network formation. This integrative approach can strongly advance our understanding about the formation of various networks.

My research focuses on building a systemic understanding of coupled human-natural systems. In particular, I am interested in understanding how patterns of land-use and land-cover change emerge from human alterations of natural processes and the resulting feedbacks. Study systems of interest include those undergoing agricultural to urban conversion, typically known as urban sprawl, and those in which protective measures, such as wildfire suppression or flood/storm impact controls, can lead to long-term instability.

Dynamic agent- and process-based simulation models are my primary tools for studying human and natural systems, respectively. My past work includes the creation of dynamic, process-based simulation models of the wildland fires along the urban-wildland interface (UWI), and artificial dune construction to protect coastal development along a barrier island coastline. My current research involves the testing, refinement, extension of an economic agent-based model of coupled housing and land markets (CHALMS), and a new project developing a generalized agent-based model of land-use change to explore local human-environmental interactions globally.

I am a spatial (GIS) agent-based modeler i.e. modeler that simulates the impact of various individual decisions on the environment. My work is mainly methodological i.e. I develop tools that make agent-based modeling (ABM) easier to do. I especially focus on developing tools that allow for evaluating various uncertainties in ABM. One of these uncertainties are the ways of quantifying agent decisions (i.e. the algorithmic representation of agent decision rules) for example to address the question of “How do the agents decide whether to grow crops or rather put land to fallow?”. One of the methods I developed focuses on representing residential developers’ risk perception for example to answer the question: “to what extent is the developer risk-taking and would be willing to build new houses targeted at high-income families (small market but big return on investment)?”. Other ABM uncertainties that I evaluate are various spatial inputs (e.g. different representations of soil erosion, different maps of environmental benefits from land conservation) and various demographics (i.e. are retired farmers more willing to put land to conservation?). The tools I develop are mostly used in (spatial) sensitivity analysis of ABM (quantitative, qualitative, and visual).

My research interests primarily lie at the interface of ecology and epidemiology, and include host-pathogen systems that are of public health or conservation concern. I use ecologic, epidemiologic and model-based investigations to understand how pathogens spread through, persist in, and impact host populations. Animal disease systems that I am currently working on include canine rabies, leptospirosis, chronic wasting disease, bighorn sheep pneumonia, raccoon roundworm (Baylisascaris procyonis), chytridiomycosis, and Lyme disease.

I am an Associate Professor in the Department of Geography at the University of Montreal. My research interests center around the incorporation of artificial intelligence and machine learning techniques into the development Agent-Based Models to solve complex socio-ecological problems in different kind of systems, such as urban, forest and wetland ecosystems.

The core of my research projects aim to learn more about spatial and temporal interactions and relationships driving changes in our world, by focusing on the multidisciplinary nature of geographical information science (GIScience) to investigate the relationships between ecological processes and resulting spatial patterns. I integrate spatial analysis and modeling approaches from geographic information science (GIScience) together with computational intelligence methods and complex systems approaches to provide insights into complex problems such as climate change, landscape ecology and forestry by explicitly representing phenomena in their geographic context.

Specialties: Agent-based modeling, GIScience, Complex socio-environmental systems, Forestry, Ecology