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H. Van Dyke Parunak
AxonAI, Inc.
Harrisonburg, Virginia
 

H. Van Dyke Parunak is Vice President for Technology Innovation at AxonAI, Inc. He has extensive experience in both academic and industrial environments with chaos and complex systems, artificial intelligence, distributed computing, and human interfaces. Since 1984, he led the Agent-based and Complex Systems Research group, housed since 1984 in a variety of companies, studying swarm intelligence, emergent behavior, and nonlinear dynamics. In 2013, AxonAI was formed to commercialize these results.

His major research accomplishments include fine-grained agent architectures for a variety of manufacturing and defense functions, emphasizing applications of nonlinear dynamics and chaos theory to analyzing and controlling agent communities. He and his colleagues develop applications that take advantage of the self-organizing potential of very simple agents when they interact through a shared environment, inspired by examples in biological and ecological systems. He is the author or co-author of more than 200 technical articles and reports, and the coinventor on 13 patents in the area of agent technology.

Parunak has worked as a project designer and associate investigator at Harvard University, as an Assistant Professor and Postdoctoral Scholar at the University of Michigan, and as a computer scientist with Comshare, Inc. His research was done at the Industrial Technology Institute, ERIM, Altarum, NewVectors, TechTeam Government Solutions, Jacobs Technology Group, and Soar Technology. He received an AB in Physics from Princeton University (1969), an MS in Computer and Communication Sciences from the University of Michigan (1982), and a PhD in Near Eastern Languages and Civilizations from Harvard University (1978). He is a member of the American Association for Artificial Intelligence and the Association for Computing Machinery.

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Updated March 1, 2016


     

Trends in social network analysis: information propagation, user behavior modeling, forecasting, and vulnerability assessment
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Analysis of graphical data (composed of nodes with edges between them) is a particularly challenging facet of the big data problem. Relational data (tables defining the features of each of a set of entities) enjoy computationally efficient algorit...

 

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A wise man (reportedly Yogi Berra, but more likely a legislator in the Danish parliament) once observed, “Prediction is difficult, especially of the future.” In spite of the challenge, humanity’s oldest written records attest the...

 
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