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Agent Based Modeling–Researching Infrastructure Interdependencies
Tuul Triyason is an Assistant Professor at the School of Information Technology, King Mongkut’s University of Technology Thonburi (KMUTT), Thailand. He holds a B.Sc. in Physics from Mahidol University and M.Sc. and Ph.D. degrees in Information Technology from KMUTT. His research and teaching focus on cybersecurity and computer networks. He serves as an IT consultant to multiple Thai government agencies, including the Thai Customs Department, the Department of Land Transport, the Office of the Attorney General, and the Department of Foreign Trade. His research interests include public sector IT procurement policy, digital infrastructure governance, and anti-corruption in government IT investment.
Computer network, Network Security, Cybersecurity, Computational model, Identity Management, Blockchain, VC-DID
I study how networked human, machine, and hybrid systems produce cognition, information diffusion, error, coordination, and collective action. My work connects social networks, organizational behavior, computational social science, experimental network science, behavioral measurement, AI-mediated communication, and applied research infrastructure.
• GIS Analyst / GIS Specialist: Experienced in applying GIS tools and spatial analysis techniques to
support decision-making in urban planning, environmental management, transportation, and infrastructure
projects. Skilled in producing high-quality maps, conducting spatial analysis, and delivering actionable
geospatial insights for operational and policy use.
• Geospatial Data Scientist: Specialized in developing spatial predictive models by integrating machine
learning and geospatial data to perform risk assessment, suitability analysis, and forecasting using large-
scale datasets such as satellite imagery, climate variables, and land-use data.
• Spatial Data Engineering & Processing: Strong ability to manage and preprocess complex geospatial
datasets, including raster and vector data, DEMs, remote sensing products, and climate projections, with
rigorous attention to spatial reference systems, accuracy, and data quality control.
• GIS Workflow Automation & Optimization: Proven experience in automating geospatial workflows
using ArcGIS Pro, ArcPy, ModelBuilder, FME (ETL), and Python to improve efficiency in spatial analysis,
data processing, and large-scale mapping tasks.
• Remote Sensing & Earth Observation Analysis: Proficient in satellite imagery processing and analysis,
including cloud masking, spectral analysis, vegetation indices, land cover classification, and temporal
change detection using Google Earth Engine and Python.
• Geospatial Visualization & Cartography: Skilled in producing professional-grade thematic maps, spa-
tial dashboards, and web-based geovisualizations to communicate complex geospatial patterns to both
technical and non-technical stakeholders.
• Cross-Disciplinary Collaboration: Experienced working with multidisciplinary teams across academia,
government, and industry to deliver geospatial solutions for planning, environmental risk assessment, and
policy-driven decision-making.
Dr. Saeed Moradi received his Ph.D. in Civil Engineering from Texas Tech University in Lubbock, Texas. Saeed has 11+ years of experience in research, policymaking, housing sector, construction management, and structural engineering. His career developed his enthusiasm for the enhancement of post-disaster recovery plans. Through his research on disaster recovery, community resilience, and human-centered complex systems, Saeed aims to bridge the gap between social sciences and civil/infrastructure engineering.
Community and Infrastructure Resilience
Disaster Recovery
Complex Systems Modeling
Agent-Based Modeling
System Dynamics
Machine Learning
Pattern Recognition
Data Mining
Spatial Analysis and Modeling
Construction Management
Building Information Modeling
Peter Gerbrands is a Researcher at the of Utrecht University School of Economics, where is develops the data infrastructure FIRMBACKBONE. He teaches data science courses and econometrics as well as supervising bachelor, master, and Ph.D. theses. His research interests are agent-based simulations, social network analysis, complex systems, big data analysis, statistical learning, and computational social science. He applies his skills primarily for policy analysis, especially related to illicit financial flows, i.e. tax evasion, tax avoidance and money laundering and has published in Regulation & Governance, and EPJ Data Science. Prior to becoming an academic, Peter had a long career in IT consulting. In the Fall of 2023, he was a Visiting Research Scholar at SUNY Binghamton in NY.
agent-based simulations
social network analysis
complex systems
big data analysis
statistical learning
computational social science
Eric has graduate degrees in urban planning and policy and sociology and an undergraduate degree in biology. He has worked on multiple collaborative and interdisciplinary projects and is skilled at engaging communities and other stakeholders. He is adept at qualitative research and has earned a Certificate in Geospatial Analysis and Visualization, demonstrating proficiency in Adobe Suite, ArcGIS, agent-based modeling and system dynamics modeling. He is currently writing manuscripts for publication based on his work on motivating energy retrofit decisions, energy-related urban planning, municipal decision-making on infrastructure investments, and other work on resilience and sustainability.
Conducts urban planning and policy research on energy efficiency, environmental, and infrastructure decision making.
I obtained a PhD in database information theory from the University of the West of Scotland in 2015, and have been a researcher at the James Hutton Institute ever since. My areas of research are agent-based-modelling (ABM), data curation, effective use of infrastructure as a service (IaaS), and semantic information representation and extraction using formal structures such as computerised ontologies, relational databases and any other structured or semi-structured data representations. I primarily deal with social and agricultural models and was originally taken on in the role of knowledge engineer in order to create the ontology for the H2020 project, Green Lifestyles, Alternative Models and Upscaling Regional Sustainability (GLAMURS). Subsequent work, for the Scottish Government has involved the use of IaaS, more commonly referred to as the “cloud” to create rapidly deployable and cheap alternatives to in-house high-performance computing for both ABM and Geographical Information System models.
It is the mixture of skills and interests involving modelling, data organisation and computing infrastructure expertise that I believe will be highly apposite in the duties associated with being a member of the CoMSES executive. Moreover, prior to joining academia, I spent about 25 years as a developer in commercial IT, in the agricultural, entertainment and banking sectors, and feel that such practical experience can only benefit the CoMSES network.
Dr. Kimberly G. Rogers studies the coupled human-natural processes shaping coastal environments. She obtained a B.Sc. in Geological Sciences from the University of Texas at Austin and began her graduate studies on Long Island at Stony Brook University’s School of Marine and Atmospheric Sciences. Rogers completed her Ph.D. at Vanderbilt University, where she specialized in nearshore and coastal sediment transport. She was a postdoctoral scholar and research associate at the Institute for Arctic and Alpine Research at the University of Colorado Boulder. In 2014, her foundation in the physical sciences was augmented by training in Environmental Anthropology at Indiana University Bloomington through an NSF Science, Engineering, and Education for Sustainability (SEES) Fellowship.
Rogers’s research is broadly interdisciplinary and examines evolving sediment dynamics at the land-sea boundary, principally within the rapidly developing river deltas of South Asia. As deltas are some of the most densely populated coastal regions on earth, she incorporates social science methods to examine how institutions — particularly those governing land use and built infrastructure — influence the flow of water and sediment in coastal areas. She integrates quantitative and qualitative approaches in her work, such as direct measurement and geochemical fingerprinting of sediment transport phenomena, agent-based modeling, institutional and geospatial analyses, and ethnographic survey techniques. Risk holder collaboration is an integral part of her research philosophy and she is committed to co-production and capacity building in her projects. Her work has gained recognition from policy influencers such as the World Bank, USAID, and the US Embassy Bangladesh and has been featured in popular media outlets such as Slate and Environmental Health Perspectives.
IRPact - An integrated agent based modeling approach in innovation diffusion
Goal: The goal of IRPact is to develop a flexible and generic innovation-diffusion ABM (agent-based modelling) framework, based on requirements derived from a literature analysis. The aim of IRPact is to allow for modeling a large number of application contexts and questions of interest.
It provides a formal model (framework) as well as a software implementation in order to assist modelers with a basic infrastructure for their own research.
Conceptually it is thought to be part of the IRPsim (https://irpsim.uni-leipzig.de), with the vision to bring together rational approaches and cognitive modeling in an integrated approach within the context of sustainable energy markets.
Displaying 10 of 12 results infrastructure clear search