Agent-based computing in economics and finance
Large-scale agent-based models
Agent models calibrated by micro-data
Complex adaptive systems
Mathematical analysis of agent systems
Mainly interested in studying social networks of learners, teachers, and innovators. Uses Social Network Analysis, but also sentiment analysis, data mining, and recommender system techniques.
Ecological modeller; behaviour of pollinating insects (especially bumblebees) in GIS landscapes. Hope to apply ABM methods to model some of the field data we have collected
social-ecological modelling; cognitive modelling; agent-based modeling&simulation; data science; smart city modelling; artificial intelligence; large-scale simulation
Down Networks is a real time, progressively agile non profit startup whose goals are to fund its research via pragmatically aggressive altruistic entrepreneurial pursuits informed by proprietary in-house techniques, open source technology and refined scientific methodology.
I am a full stack software engineer that has been building cyberinfrastructure for computational social science at Arizona State University since 2006; projects include the Digital Archaeological Record, the Virtual Commons, the Social Ecological Systems Library, Synthesizing Knowledge of Past Environments (SKOPE) and CoMSES Net, where I serve as co-director and technical lead.
I’m also a Software / Data Carpentries certified instructor and maintainer for the Python Novice Gapminder lesson, and member of the Force 11 Software Citation Implementation Working Group.
My research interests include collective action, social ecological systems, large-scale software systems engineering, model componentization and coupling, and finding effective ways to promote and facilitate good software engineering practices for reusable, reproducible, and interoperable scientific computation.
My experience is diverse, with research in spatial analyses and GIS, ecosystem modeling, landscape ecology, database management, biogeographical relationships of birds and plants, species/habitat relationships, wildlife and pastoral livestock mobility, spectroscopy, cluster analysis, and telemetry techniques. Research projects are ongoing in Colorado, the contiguous US, Kenya, Mali, and Tibet.
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.
Sedar will be focusing on data analytics and smart cities, developing a programming library to try simulate how policies can impact a small world of autonomous intelligent agents to try deduce positive or negative impact in the long run. If the impact is positive and this is conveyed collectively taking into consideration the agent’s health, happiness and other social characteristics then the policy can be considered. Furthermore, he will work on agent based modelling to solve and provide faster solutions to economic and social elements of society, establishing applied and theoretical answers. Some other interests are:
One of my research areas is agent-based modelling of land change in Brazil. I have worked with ABM in frontier areas of the Brazilian Amazon. I am also part of the team that develops TerraME, an OSS toolkit for ABM in cellular spaces.