I am a computational archaeologist and Professor of Anthropology at San Diego State University, where I direct the Computational Archaeology Laboratory. My research integrates geospatial analysis, agent-based and simulation modeling, and complex adaptive systems theory to investigate long-term human–environment interactions, with particular attention to socio-environmental change associated with early farming and herding in Mediterranean and other semi-arid landscapes. I have conducted field and modeling research in regions including Italy, Jordan, and Central Asia, and my work spans landscape archaeology, land-use dynamics, and environmental modeling. I have been a member of the CoMSES community for well over a decade and have contributed multiple models to the Computational Model Library, several of which have undergone formal peer review. In addition to research, I regularly teach with agent-based models at undergraduate and graduate levels and use CoMSES models as both research and pedagogical resources. I am committed to open, reproducible, and theoretically informed computational modeling and to strengthening the role of peer-reviewed models as durable scholarly contributions.

SHIPENG SUN is an Assistant Professor in the Department of Geography and Environmental Science at Hunter College and the Earth and Environmental Sciences Program at Graduate Center, The City University of New York, New York, NY 10065. E-mail: shipeng.sun@hunter.cuny.edu.

Eletronic Engineer with specialization in Computer Science and a passion for Artificial Intelligence, Simulation, Programming, and many other tech topcis . One life is really not enough to learn and experiment all cool things that are out there. Love also learning languages: Portuguese, English, French, Italian, and German.

This is Saeed Abdolhosseini. I am very interested in the area of agent based modeling and it is about 3 years that I am working on Agent-Based Modeling. I have a good experience of working with Netlogo &Repast simphony & Anylogic. I have developed a few ABM application.

Specialties: Agent-based models of social systems

I am a researcher in data science for sustainability, working at the intersection of society, politics, economy, and the environment. My work integrates statistics, artificial intelligence, and complex systems approaches to generate robust, data-driven evidence that supports decision-making in complex socio-environmental contexts.

My research focuses on understanding and modeling socio-ecological systems, with the goal of improving sustainability outcomes through interdisciplinary analysis and innovative analytical tools.

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.

Sr Machine Learning Engineer, Google Developer Expert in Cloud and Machine Learning. CompTIA Security+, AWS certified Machine Learning specialty.