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George Sugihara (born in Tokyo, Japan) is a professor of biological oceanography and complex systems at the Scripps Institution of Oceanography, where he is the inaugural holder of the McQuown Chair in Natural Science. Sugihara is a theoretical biologist and information scientist who works across a variety of fields ranging from ecology, to epidemiology, genetics, geoscience, network theory, nonlinear dynamics, atmospheric science, quantitative finance and economics.

Education
Sugihara studied natural resources at the University of Michigan, where he received a BS in 1973. In 1978, he matriculated at Princeton University, where he studied mathematical ecology under Robert May, earning an MS in biology in 1980 and PhD in mathematical biology in 1983.

While at Princeton, Sugihara contributed to species abundance by identifying regularities in hierarchical community structure expressed by sequentially divided niches. The hierarchical structure, representing a minimal form of community structure, derives from evolutionary and ecological drivers generating species diversity and accounting for observed abundance structures.

Career
Sugihara began his career as the Wigner Prize Fellow at Oak Ridge National Laboratory and concurrently associate professor of Mathematics at the University of Tennessee. A notable contribution was the topological / graph theoretical proof that increasing food web species specialization combined with the rigid circuit property leads to the rule that species enter communities by attaching within individual guilds or cliques rather than across multiple guilds.

In 1986, he joined Scripps Institution of Oceanography (SIO), holding the UC San Diego John Dove Isaacs Chair in Natural Philosophy from 1990 to 1995. Since 2007 he has been the McQuown Professor of Natural Science at Scripps.

Sugihara has been a visiting professor at Cornell University, Imperial College London, Kyoto University and the Tokyo Institute of Technology, and was a visiting fellow at Merton College, Oxford University in 2002. He served as a member of the National Academy of Sciences Board on Mathematical Sciences and its Applications.

His initial work on fisheries as complex, chaotic systems led to work on financial networks and prediction of chaotic systems, laying the foundation for empirical, data-driven methods to analyze and forecast complex systems.

From 1997–2002, Sugihara took leave from SIO to work at Deutsche Bank on quantitative finance as a Managing Director. He helped found Prediction Company and Quantitative Advisors LLC, and has been a consultant to the Bank of England, the Federal Reserve Bank of New York, and to the Federal Reserve System on questions of international security regarding systemic risk in the financial sector.

In 2008 he was interviewed and subsequently solicited by the Obama administration for the position of Chief Scientist of NOAA, but declined to pursue the position.

Contributions
His wide-ranging contributions include natural resource management and policy development. He was commissioned by the Eastern Bering Sea and Aleutian Islands Alaskan Pollock Fleet, one of the most valuable fisheries in the world to design a market-incentive plan, the Comprehensive Incentive Plan (CIP), for salmon by-catch avoidance. He developed the plan framework implemented in 2010 to protect the native salmon fisheries of western Alaska, resulting in a marked decrease in salmon bycatch.

Other contributions address topology and assembly of ecological systems, and, social system dynamics, as well as work on generic early warning signs of critical transitions across many apparently different classes of systems.

Empirical Dynamic Modeling
Sugihara's focus on data-driven, practical solutions to analysis and forecasting of complex systems has developed the empirical dynamic modeling paradigm, a model-free, state space based set of tools and techniques widely applicable to complex, nonlinear systems. A particularly important and widely-used tool is convergent cross mapping, a method to quantify cause-and-effect relationships as expressed in the underlying dynamics of the data rather than on statistical estimates such as a correlation coefficient that may not be justifiable or informative on nonlinear (state-dependent) complex systems.

Research interests
His research interests include nonlinear dynamics, complex systems, complexity theory, nonlinear forecasting, food web structure, species abundance topology, conservation biology, biological control, neuroscience, empirical climate modelling, fisheries forecasting, and the design and implementation of derivatives markets for fisheries.