About me
Name: Dr. Julia Kasmire
Position: Post-doctoral Research Fellow
Where: UK Data Services and Cathie Marsh Institute at the University of Manchester.
Short Bio
2004 - BA in Linguistics from the University of California in Santa Cruz, including college honours, departmental honours and one year of study at the University of Barcelona.
2008 - MSc in the Evolution of Language and Cognition from the University of Edinburgh, with a thesis on the effects of various common simulated population features used when modelling language learning agents.
2015 - PhD from Faculty of Technology, Policy and Management at the Delft University of Technology under the supervision of Prof. dr. ig. Margot Wijnen, Prof. dr. ig. Gerard P.J. Dijkema, and Dr. ig. Igor Nikolic. My PhD thesis and propositions can be found online, as are my publications and PhD research projects (most of which addressed how to study transitions to sustainability in the Dutch horticultural sector from a computational social science and complex adaptive systems perspective).
Additional Resources
Many of the NetLogo models I that built or used can be found here on my CoMSES/OpenABM pages.
My ResearchGate profile and my Academia.org profile provide additional context and outputs of my work, including some data sets, analytical resources and research skills endorsements.
My LinkedIn profile contains additional insights into my education and experience as well as skills and knowledge endorsements.
I try to use Twitter to share what is happening with my research and to keep abreast of interesting discussions on complexity, chaos, artificial intelligence, evolution and some other research topics of interest.
You can find my SCOPUS profile and my ORCID profile as well.

My primary research interests lie at the intersection of two fields: evolutionary computation and multi-agent systems. I am specifically interested in how evolutionary search algorithms can be used to help people understand and analyze agent-based models of complex systems (e.g., flocking birds, traffic jams, or how information diffuses across social networks). My secondary research interests broadly span the areas of artificial life, multi-agent robotics, cognitive/learning science, design of multi-agent modeling environments. I enjoy interdisciplinary research, and in pursuit of the aforementioned topics, I have been involved in application areas from archeology to zoology, from linguistics to marketing, and from urban growth patterns to materials science. I am also very interested in creative approaches to computer science and complex systems education, and have published work on the use of multi-agent simulation as a vehicle for introducing students to computer science.

It is my philosophy that theoretical research should be inspired by real-world problems, and conversely, that theoretical results should inform and enhance practice in the field. Accordingly, I view tool building as a vital practice that is complementary to theoretical and methodological research. Throughout my own work I have contributed to the research community by developing several practical software tools, including BehaviorSearch (http://www.behaviorsearch.org/)

My research uses modeling to understand complex coupled human and natural systems, and can be generally described as computational social science. I am especially interested in modeling water management systems, in both archaeological and contemporary contexts. I have previously developed a framework for modeling general archaeological complex systems, and applied this to the specific case of the Hohokam in southern Arizona. I am currently engaged in research in data mining to understand contemporary water management strategies in the U.S. southwest and in several locations in Alaska. I am also a developer for the Repast HPC toolkit, an agent-based modeling toolkit specifically for high-performance computing platforms, and maintain an interest in the philosophy of science underlying our use of models as a means to approach complex systems. I am currently serving as Communications Officer for the Computational Social Science Society of the Americas.

Dr. Lilian Alessa, University of Idaho President’s Professor of Resilient Landscapes in the Landscape Architecture program, is also Co-Director of the University of Idaho Center for Resilient Communities. She conducts extensive research on human adaptation to environmental change through resilient design at landscape scales. Much of her work is funded by the National Science Foundation, including projects awarded the Arctic Observing Network, Intersections of Food, Energy and Water Systems (INFEWS) and the Dynamics of Coupled Natural Human Systems programs. Canadian-born and raised, Alessa received her degrees from the University of British Columbia. She also uses her expertise in social-ecological and technological systems science to develop ways to improve domestic resource security for community well-being, particularly through the incorporation of place-based knowledge. Her work through the Department of Homeland Security’s Center of Excellence, the Arctic Domain Awareness Center, involves developing social-technological methods to monitor and respond to critical environmental changes. Lil is a member of the National Science Foundation’s Advisory Committee for Environmental Research and Education and is on the Science, Technology and Education Advisory Committee for the National Ecological Observing Network (NEON). Professor Alessa also teaches a university landscape architecture capstone course: Resilient Landscapes with Professor Andrew Kliskey. Professor Alessa’s collaborative grant activity with Professor Andrew Kliskey, since coming to the university in 2013, exceeds 7 million USD to date. She has authored over a 100 publications and reports and has led the development of 2 federal climate resilience toolbox assessments, the Arctic Water Resources Vulnerability Index (AWRVI) and the Arctic Adaptation Exchange Portal (AAEP).

I am a researcher in computational policy modelling and AI Governance Lead at City Business AI (Traavu Ltd). I build agent-based models that test how a policy distributes access, delay and protection across a real population, including IPS-England, a model of England’s SEND identification and assessment pathway, and the GRID Observatory, on safeguarding underreporting in specialist SEND schools. My path runs through investigative journalism, health data infrastructure at Orodata Science in Nigeria, and practice in specialist SEND education. I am a UKRI Future Leaders Fellowship nominated candidate hosted at Leeds Beckett Law School.

John E. McEneaney is Professor Emeritus of Learning and Teaching in the School of Education and Human Services at Oakland University, Rochester, MI, USA.

I am currently enrolled as a graduate student at UC3M, working towards a MS degree in Computational and Applied Mathematics. Upon completing my current program, my intention is to further my education in Applied Economics, with a specific focus on the intersection of Climate and Development Economics.

My research pursuits center around investigating the impacts of climate change on developing nations. Additionally, I am interested in studying the repercussions of fast fashion consumption, examining its effects on working conditions, the environment, and the overall well-being of individuals in the countries where these garments are manufactured. In my ongoing master’s thesis, I employ Agent-Based Modeling to simulate the attitudes of individual consumers towards fast fashion. The model captures behavioral shifts influenced by peers, social media, and governmental factors. This research aligns with my broader interests in comprehending public perspectives on global matters, underscoring the crucial influence of individual attitudes in confronting and finding solutions to these challenges.