SFI Networks Short Course 201 - Faculty 2017

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SFI NETWORKS & BIG DATA SHORT COURSE ON COMPLEXITY

July 26-28, 2017 - Hyatt Centric Times Square, New York City, New York

DIRECTOR

Short Course Director Michelle Girvan

Michelle Girvan is an Associate Professor in the Department of Physics and the Institute for Physical Science and Technology at the University of Maryland, College Park. She is also a member of the External Faculty at the Santa Fe Institute. Her research operates at the intersection of statistical physics, nonlinear dynamics, and computer science and has applications to social, biological, and technological systems. More specifically, her work focuses on complex networks and often falls within the fields of computational biology and sociophysics. While some of the research is purely theoretical, Girvan has become increasingly involved in using empirical data to inform and validate mathematical models.
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FACILITATOR

Short Course Facilitator David Darmon

Applied Mathematician - University of Maryland, College Park. Traumatic Injury Research Program - The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. (HJF), which is a global organization dedicated to advancing military medical research.
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SPEAKERS CONFIRMED TO DATE

Damon Centola - Annenberg School for Communication - University of Pennsylvania

Damon Centola's research addresses the complex dynamics of collective behaviors. One set of projects uses agent based models to understand how changes in the topology of social networks can impact the spread of social contagions. Other work uses web-based experimental methods to test these models and provide new theoretical insights into research on collective behavior. Research areas include the emergence of unpopular norms, the mobilization of committed minorities, and the polarization of cultures.

This research has won the 2006, 2009 and 2011 American Sociological Association Award for Outstanding Article in Mathematical Sociology, and the 2011 Goodman Award for Outstanding Contributions to Sociological Methodology.
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Peter Dodds - Professor, Director of the Vermont Complex Systems Center and More - University of Vermont

Peter Dodds is a Scientist at the University of Vermont (UVM). Together with Chris Danforth, he co-runs the Computational Story Lab. He is the Director of the Vermont Complex Systems Center at UVM (VCSC). I'm a Full Professor in the Department of Mathematics and Statistics which is part of the College of Engineering and Mathematical Sciences at UVM. He is also a visiting faculty fellow at the Vermont Advanced Computing Core (VACC).

In his research and teaching, Dodds focuses on system-level, big data problems of all kinds, often networked, sociotechnical ones. He has general interest in stories/narratives, complexification, contagion, and robustness, and a long term goal to understand a Theory of Anything.

CV website

Neo Martinez - University of Arizona

Neo Martinez' Lab investigates the structure and function of complex networks, especially ecological networks involving feeding relationships, population dynamics, evolution and interactions with humans. He is a broadly trained interdisciplinary ecologist who employs empirically and theoretically oriented computational tools including simulations, visualizations, informatics, and games to elucidate the complex interdependencies of all life on earth.
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Ole Peters - SFI External Faculty and Resident Fellow - London Mathematical Laboratory

Ole Peters's work focuses on stochastic processes that are non-ergodic. This means that long-time averages do not converge to expectation values. Such processes are of practical relevance because they form the basis of economic theory. However, their non-ergodic nature has not been fully explored.

Prior to becoming interested in the foundations of economics I worked on atmospheric convection and far-from equilibrium critical phenomena in statistical mechanics.

Extra-curricular: I’m a fan of anything to do with oceans.
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Bill Rand - Poole College of Management - NC State

William Rand examines the use of computational modeling techniques, such as agent-based modeling, machine learning, network analysis, natural language processing, and geographic information systems, to help understand and analyze complex systems, such as the diffusion of information, organizational learning, and economic markets.

He also works to develop methods, create pedagogy, and build frameworks to allow researchers and practitioners to use analytics and data-intensive methods in their own work. He has received funding for his research from the NSF, DARPA, ARL, Google, WPP, and the Marketing Science Institute. His work has been published in JM, JMR, IJRM, Management Science, and JOM. He received his doctorate in Computer Science from the University of Michigan in 2005 and prior to coming to NCSU was at the University of Maryland for eight years.
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