Curtis Atkisson

Curtis Atkisson joined the eScience Institute in Feb 2024. He received his PhD in Evolutionary Anthropology from UC Davis with a designated emphasis in Computational Social Science. His dissertation was on how changes in people’s complex social networks impact their cooperative behavior. This work involved ethnography, participant observation, and surveys done amongst the Makushi (and some Wapashana) in southern Guyana. The analysis of those data required complex networks, designing new measures of information in those networks, and modeling those changes with custom-built Bayesian statistical models. His postdoctoral work applied his methodological expertise to understanding Open Source Software communities and how they persist, as well as expanding his tools to include Text As Data/Text Mining, machine learning, and AI approaches to understanding text (e.g., GPT as a zero-shot translator).

Curtis brings a broad and mixed set of both qualitative and quantitative methods that can be used to understand dynamic processes as well as their surrounding contexts. His approach to statistics is to maximize the information that can be drawn from data by developing custom-built Bayesian models that can use MCMC to reason about quite diverse data-generating mechanisms. He is constantly pushing the boundaries of his statistical methods to find the best solution for any given research question. He has worked extensively with qualitative data analysis methods, especially content analysis and grounded theory, and machine analysis of qualitative data, especially machine learning natural language processing methods and large language model approaches. He has an emphasis on network analysis, including “standard” networks, complex networks (e.g., multiplex/multi-layer, temporal, etc.), and networks for policy analysis (e.g., Networks of Prescribed Interactions).

Substantively, Curtis is interested in why people cooperate with others, or, perhaps more accurately, why we do not always cooperate with others. This ranges from small-scale cooperation such as sharing food with a neighbor up to large-scale cooperation such as building computer software that we give away for free that makes other people rich. He is also interested in using open-source software as a model system to study many processes in the social sciences–due to the nature of open-source software, almost all of the data regarding interactions within and participation in the system are publicly available. If you have a social science question for which you cannot gather complete enough data, he would love to work with you to see if the idea can be tested in the open-source software data!


  • Bayesian statistics
  • Social Network Analysis
  • Qualitative data analysis
  • Mixed methods approaches
  • Text As Data
  • R