TedX meeting, October 28, 2011, Davis Auditorium, UVM
In the last few hundred years we have discovered the many hidden components of natural complex systems—atoms, microbes, DNA—as well as described better those complex elements in plain sight such as animals and people. And in more recent decades, new forms of complexity have quickly arisen due to the interconnectedness of computers, power stations, economies, and human organizations at all scales, with the consequent boons—easy access to data, collaborative online innovation—and banes—large-scale blackouts, financial collapses. The time frame of 1700 to 2000 may well be looked upon as the golden age of reductionism, a crucial period in the history of science that has led us to the necessity of understanding and creating complex systems.
We framed our inaugural meeting around Big Data of all kinds. By the first decade of the 21st Century, many sciences had transformed from being relatively data-scarce to almost overwhelmingly data-rich: biology, astronomy, medicine and, more and more, the social sciences. What are the kinds of stories we can find and tell with these enormous data sets?
Speaker bios and videos of talks:
Peter Dodds is a scientist in the Dept. of Mathematics & Statistics working on large-scale, system-level problems in many fields including sociology, geomorphology, biology, and ecology.
For more information, please see his website.
Professor Rob Axtell works at the intersection of economics, behavioral game theory, and multi-agent systems computer science. His most recent research attempts to emerge a macroeconomy from tens of millions of interacting agents. He is Chair of the Dept. of Computational Social Science at George Mason University and External Professor at the Santa Fe Institute. For more information, please see his website.
Professor Marta González (MIT) integrates methods of complex systems with statistical physics approaches, computational sciences, geographic information systems and network theory to characterize and model human dynamics. Her current research explores human mobility patterns and analyzes network organization in relation to its attributes in social systems and spreading dynamics. For more information, please see her website.
Professor Neil Johnson heads up a new inter-disciplinary research group in Complexity at University of Miami (Physics Dept.) looking at collective behavior and emergent properties in a wide range of real-world Complex Systems: from the physical, biological, medical domains through to social and even financial domains. For more information, please see his website.
Isabel Kloumann explores the interplay between language and emotion by analyzing massive digital texts with a combination of human- and silicon-based supercomputers (see Amazon Mechanical Turk and the Vermont Advanced Computing Center). She is developing metrics for measuring happiness in digital human expressions, namely in Twitter data. She is a Ph.D. candidate at Cornell University, and an alumna of UVM’s Dept. of Mathematics & Statistics and Dept. of Physics.
Mike Schmidt works in the Cornell Computational Synthesis Lab (CCSL) at Cornell University. His research includes symbolic regression and related evolutionary algorithms. He is the co-designer of Eureqa, a free software tool for detecting equations and hidden mathematical relationships in data. Its goal is to identify the simplest mathematical formulas which could describe the underlying mechanisms that produced the data. For more information, please see his website.
Josh Bongard’s work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science. For more information, please see his website.
Chris Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter. He also helps run UVM’s reading group on complexity. For more information, please see his website.
Hugh Garavan’s research interest is cognitive neuroscience, with a focus on cognitive control functions. This interest merges naturally into clinical questions regarding the neurobiology underlying control dysfunction. Related research interests concern the processes underlying the development of cognitive functions and how these might contribute to the psychopathologies that emerge during adolescence. For more information, please see his departmental biography.
Gary Johnson models the spatial flow of services from ecosystems to people and changes to these services by different management scenarios. His research interests include ecosystem service modelling and simulation, network flow optimization and substitutability, hierarchical modelling, stochastic processes and uncertainty propagation, decision and game theory, functional programming, and machine learning. For more information, see http://ariesonline.org.
Austin Troy’s work focuses on land use — particularly the causes and impacts of urban/suburban development and the effectiveness of policies in mediating those impacts. He is also interested in studying the role and importance of environmental assets in urban systems and in quantificating the spatial distribution of ecosystem services. For more information, please see his website.