Research Interests

I am a complex systems scientist in the Schools of Human Evolution & Social Change and Complex Adaptive Systems at Arizona State University. My research interests center around long-term human ecology, landscape dynamics, and the multi-dimensional interactions between social and biophysical systems. I have long running collaborative projects in the Mediterranean (Upper Pleistocene through mid-Holocene), and have done fieldwork in Spain, Bosnia, the American Southwest, and various other locales in North America. My work spans hunter-gatherer and early farming societies, geoarchaeology, lithic technology, and evolutionary theory, with an emphasis on human/environmental interaction, landscape dynamics, and techno-economic change. Quantitative and computational methods are critical to complex systems science, archaeological research, and socioecological sciences in general. They play an integral role my research, especially computational modeling, geospatial technologies (including GIS and remote sensing), data science, and visualization.

Open Science

I am an advocate for open, transparent science. As Director of CoMSES.Net and the Open Modeling Foundation, I endeavor to promote open scientific computation for social and ecological sciences. I am also member of the open source GRASS GIS international development team and Project Steering Committee that is making cutting edge spatial technologies available to researchers and students around the world. To help in this effort, I maintain GRASS binary apps for the Macintosh OS at the GRASS for Mac web site.

Current Research

MedLanD Project

The Mediterranean Landscape Dynamics project is an interdisciplinary research project to study the long-term interactions of humans and landscapes in the Mediterranean. This work combines interdisciplinary fieldwork and computational modeling with support from the NSF Coupled Natural and Human Systems program.


The Network for Computational Modeling in the Social and Ecological Sciences (CoMSES.Net) aims to improve accessibility by social and ecological scientists to emerging cybertools for studying the dynamics of the socio-ecological systems that dominate the earth today. CoMSES Net is a node in the NSF national big data infrastructure network. CoMSES.Net is now supported by the NSF Cyberinfrastructure for Sustained Scientific Innovation program to expand education and training, and develop new cyberfrastructure to help make scientific modeling FAIR. This is a collaborative effort with partners Community Surface Dynamics Modeling System, CUAHSI, Science Gateways Community Institute, and the Open Science Grid.

Open Modeling Foundation

The Open Modeling Foundation is an international federation of modeling organizations to develop, promote and administer FAIR standards and best practices for computational modeling in the social, ecological, environmental, and geophysical sciences. This effort is supported by the Alfred P Sloan Foundation.

Humans and Climate

Two collaborative projects with the National Center for Atmospheric Research are modeling the flow of information across social networks during natural disasters with support from the NSF Hazard SEES Program and modeling the social and environmental impacts of alternative climate intervention strategies with support from the NSF Growing Convergence Research Program.

GRASS GIS Open Source Ecosystem

With support from the NSF Pathways to Enable Open-Source Ecosystems (POSE), this project aims to: 1) grow the community of researchers and geospatial practitioners in academia, governments, and industry using GRASS GIS as a key geoprocessing engine; and 2) expand and diversify the developer community, especially through supporting next-generation scientists to gain expertise to maintain and innovate GRASS software. Activities include modernization of software infrastructure and distribution, enhancing the code quality and security, improving documentation for users and contributors, and providing new training opportunities for GRASS GIS use and software development.

This research is supported by Arizona State University, the National Science Foundation, and the Alfred P Sloan Foundation