I am a Senior Economist in the Capital Markets Division of the Bank of England. I have a PhD in Economics from the joint program at Vilfredo Pareto Doctorate in Economics (University of Turin) and Collegio Carlo Alberto, where I’ve taught graduate level economic courses. Prior to joining the Bank of England, I also worked in the private sector as a quantitative analyst on issues related to different areas including asset management, risk management, and policy implementation.

Dr. Dawn Parker is a professor at the University of Waterloo in the School of Planning. Her research focuses on the development of integrated socio-economic and biophysical models of land-use change. Dr. Parker works with agent-based modeling, complexity theory, geographic information systems, and environmental and resource economics. Her current ongoing projects include Waterloo Area Regional Model (WARM) Urban intensification vs. suburban flight, a SSHRC funded development grant that explores the causal relationships between light rail transit and core-area intensification, and the Digging into Data MIRACLE (Mining relationships among variables in large datasets from complex systems) project.

Andrew J. Collins, Ph.D., is an associate professor at Old Dominion University in the Department of Engineering Management and Systems Engineering. He has a Ph.D. in Operations Research from the University of Southampton, and his undergraduate degree in Mathematics was from the University of Oxford. He has published over 80 peer-review articles. He has been the Principal Investigator on projects funded to the amount of approximately $5 million. Dr. Collins has developed several research simulations including an award-winning investigation into the foreclosure contagion that incorporated social networks.

Guido Fioretti, born 1964, graduated in Electronic Engineering in 1991 at La Sapienza University, Rome. In 1995, he received a PhD in Economics from this same university. Guido Fioretti is currently a lecturer of Organization Science at the University of Bologna.

Postdoctoral researcher at Institute of Economics, Polish Academy of Sciences and in Macroprudential Research Division at National Bank of Poland. She graduated in Mathematics (Jagiellonian University, Poland) and in Economics (University of Alcala, Spain). In 2017 she obtained Fulbright Advanced Research Award. In the United States, she carried out research on systemic risk and complex systems. Her doctoral dissertation was about the measurement and modeling of systemic risk using simulation methods and complex systems approach (the results to be published by Palgrave Macmillan US). Previously, she gained experience on agent-based modeling while working with Juan Luis Santos on the European Commission FP 7 MOSIPS project (http://www.mosips.eu/).

Malte Vogl is a senior research fellow at the Max Planck Institute of Geoanthropology with a PhD in Physics. Until recently, he worked as a research fellow and PI at the Max Planck Institute for the History of Science, in projects ranging from Digital Humanities work on the ancient perception of time and space in the cluster of excellency TOPOI, building and evalutation of research data infrastructures in context of the DARIAH project, large scale analysis of archival data for the history of the MPG project GMPG to the most recent, BMBF-funded work on method development for modelling knowledge evolution as a multilayered temporal network in the ModelSEN project.

Sedar is a PhD student at the University of Leeds, department of Geography. He graduated in Computer Science at King’s College London 2018. From a very early stage of his degree, he focused on artificial intelligence planning implementations on drones in a search and rescue domain, and this was his first formal attempt to study artificial intelligence. He participated in summer school at Boğaziçi University in Istanbul working on programming techniques to reduce execution time. During his final year, he concentrated on how argumentation theory with natural language processing can be used to optimise political influence. In the midst of completing his degree, he applied to Professor Alison Heppenstall’s research proposal focusing on data analytics and society, a joint endeavour with the Alan Turing Institute and the Economic and Social Research Council. From 2018 - 2023 he will be working on his PhD at the Alan Turing Institute and Leeds Institute for Data Analytics.

The goal of my research program is to improve our understanding about highly integrated natural and human processes. Within the context of Land-System Science, I seek to understand how natural and human systems interact through feedback mechanisms and affect land management choices among humans and ecosystem (e.g., carbon storage) and biophysical processes (e.g., erosion) in natural systems. One component of this program involves finding novel methods for data collection (e.g., unmanned aerial vehicles) that can be used to calibrate and validate models of natural systems at the resolution of decision makers. Another component of this program involves the design and construction of agent-based models to formalize our understanding of human decisions and their interaction with their environment in computer code. The most exciting, and remaining part, is coupling these two components together so that we may not only quantify the impact of representing their coupling, but more importantly to assess the impacts of changing climate, technology, and policy on human well-being, patterns of land use and land management, and ecological and biophysical aspects of our environment.

To achieve this overarching goal, my students and I conduct fieldwork that involves the use of state-of-the-art unmanned aerial vehicles (UAVs) in combination with ground-based light detection and ranging (LiDAR) equipment, RTK global positioning system (GPS) receivers, weather and soil sensors, and a host of different types of manual measurements. We bring these data together to make methodological advancements and benchmark novel equipment to justify its use in the calibration and validation of models of natural and human processes. By conducting fieldwork at high spatial resolutions (e.g., parcel level) we are able to couple our representation of natural system processes at the scale at which human actors make decisions and improve our understanding about how they react to changes and affect our environment.