Self-Organization and Robustness in Biological Systems

  • Speaker
  • Portrait photo of Corina TarnitaCorina Tarnita, Ph.D.Professor, Ecology and Evolutionary Biology, Princeton University
Date & Time

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Understanding and managing complex systems — characterized by emergent, self-organized patterns at scales larger than those of the interacting parts — has crystallized as one of the most pressing problems of our time, affecting studies in fields from biology to sociology to medicine and financial markets. Because biological systems have faced a range of challenges throughout evolutionary history that has led to a diversity of robust solutions, they are ideal for the study of complex systems, and solutions inferred from biology have successfully been applied to system design and management in other fields. It is therefore imperative not only to study individual biological systems, but also to compare broadly their organizing principles and emergent properties. The Tarnita Lab uses theoretical and empirical approaches to study the organization and emergent properties and behaviors of biological systems across spatiotemporal scales, from single cells to entire ecosystems. In this talk, Corina Tarnita will give an overview of their approach and what they have learned so far.

About the Speaker

Portrait photo of Corina Tarnita

Corina Tarnita is an associate professor of ecology and evolutionary biology at Princeton University. She joined the Princeton faculty in February 2013. Previously she was a junior fellow at the Harvard Society of Fellows (2010 – 2012). She obtained her B.A.(2006), M.A.(2008) and Ph.D. (2009) in mathematics from Harvard University. She is an Alfred P. Sloan Research Fellow, a Kavli Frontiers of Science Fellow of the National Academy of Sciences, and an Ecological Society of America Early Career Fellow. She is also the recipient of a Phi Beta Kappa Award for Excellence in Undergraduate Teaching. Her work is centered around the emergence of complex behavior out of simple interactions across spatial and temporal scales.

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