SFI Networks Short Course 201 - Faculty 2017

Networks Short Course 2017 Home Agenda Speakers Participants Readings Slides Registration Accommodations

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.
website

FACILITATOR

Short Course Facilitator David Darmon

David Darmon is a research scientist at the Uniformed Services University in Bethesda, Maryland. His research focuses on developing new analytical tools to facilitate understanding of dynamical processes that occur on networks. By realizing information theoretic analyses via nonparametric statistics, investigators can gain new insights into the organization of complex systems. He received his doctorate in Applied Mathematics and Scientific Computing from the University of Maryland, College Park in 2015.
website

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.
website




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



Daniel Larremore - Santa Fe Institute

Networks are key to describing many phenomena around and within us. From the complex signaling in the mammalian neocortex, to the patterns generated by genetic recombination, networks provide a strong foundation on which to build analytical tools that are capable of making testable predictions. However, to move beyond confirmation of known patterns into the territory of new discoveries, network techniques must be rigorously derived and rooted in strong theory. The findings of network techniques that do not overlap with those made using traditional tools are, in Daniel's view, the most valuable, and this motivates his dual focus on application and theory. more

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.
website

Hernan Makse - City College of New York, New York City

Hernan Maksi runs the Complex Networks and Soft Matter Lab of Hernan Makse at the Levich Institute and Department of Physics of City College of New York in New York City. The lab group is interested in the theoretical understanding of Complex Systems and Data Science from a Statistical Physics viewpoint. The researchers are working towards the development of new emergent laws for complex systems, ranging from the brain and biological networks to social systems. They treat these systems from a unified theoretical approach using concepts from statistical mechanics of out-of-equilibrium complex networks, optimization theory, machine learning and big-data science that we have developed in our studies of social networks and disordered systems in physics. In parallel, they also focus on the study of jammed matter, spanning from granular materials, colloidal suspensions and dense emulsions to glasses, in search of unifying theoretical frameworks. The group focuses on the theoretical and computational approaches in parallel with experiments.

website


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.
[website]

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.
website