User:Sugilab/sandbox

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George Sugihara
Sugihara in 2015
Born1949
Tokyo
Alma materPrinceton University
Known forEmpirical dynamic modeling
Scientific career
FieldsAlgebraic topology, network theory, nonlinear dynamics, chaotic systems, causality, ecology, medicine, genomics, finance, atmospheric and earth science, fisheries
InstitutionsScripps Institution of Oceanography, University of California San Diego
Cornell University
Imperial College London
Kyoto University
Tokyo Institute of Technology
Oxford University
Doctoral advisorRobert May
Doctoral studentsAlistair Hobday, Chi-Hao Hsieh, Charles Perretti, Hao Ye, Ethan Deyle, Alfredo Giron-Nava
Other notable studentsDJ Patil, Louis-Felix Bersier, Martin Casdagli, Franc Courchamp
Websitehttps://deepeco.ucsd.edu/

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.[1] 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[edit]

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.[2]

Career[edit]

Sugihara began his career as the Wigner Prize Fellow[3] 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.[4]

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.[5][6]

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.[7][8]

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[edit]

His wide-ranging contributions include natural resource management and policy development.[9] He was commissioned by the Eastern Bering Sea and Aleutian Islands Alaskan Pollock Fleet, one of the most valuable fisheries in the world[10] 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.[11]

Other contributions address topology and assembly of ecological systems, and, social system dynamics[12], as well as work on generic early warning signs of critical transitions across many apparently different classes of systems[13].

Empirical Dynamic Modeling[edit]

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[14][15]. 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 [16] rather than on statistical estimates such as a correlation coefficient that may not be justifiable or informative on nonlinear (state-dependent) complex systems.[14]


Research interests[edit]

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.

References[edit]

  1. ^ "UC Announcement of McQuown Chair". Archived from the original on 2009-01-12. Retrieved 2008-11-22.
  2. ^ Sugihara, G. (1980). "Minimal Community Structure: An Explanation of Species Abundance Patterns". The American Naturalist. 116 (6): 770–787. doi:10.1086/283669.
  3. ^ "Eugene Wigner Fellowship". www.ornl.gov. Oak Ridge National Laboratory. Retrieved 2023-08-24.
  4. ^ Sugihara, G. (1983). "Graph theory, homology, and food webs". Proc. Symp. Appl. Math. 30: 83–101. doi:10.1090/psapm/030/73864.
  5. ^ Sugihara, G.; May, R. M. (1990). "Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series". Nature. 344 (6268): 734–741. Bibcode:1990Natur.344..734S. doi:10.1038/344734a0. PMID 2330029. S2CID 4370167.
  6. ^ Sugihara, G. (1994). "Nonlinear forecasting for the classification of natural time series". Philosophical Transactions: Physical Sciences and Engineering. 348: 477–495. doi:10.1098/rsta.1994.0106.
  7. ^ Sugihara, G. 2006. Observations on new directions for understanding systemic risk in the financial sector. Federal Reserve Bank of New York and the National Academy of Science.
  8. ^ May, R.M.; Levin, S.A.; Sugihara, G. (2008). "Complex systems: Ecology for bankers". Nature. 451 (7181): 893–895. Bibcode:2008Natur.451..893M. doi:10.1038/451893a. PMID 18288170.
  9. ^ Dalton, Rex (2005). "Conservation policy: Fishy futures". Nature. 437 (7058): 473–474. Bibcode:2005Natur.437..473D. doi:10.1038/437473a. PMID 16177759.
  10. ^ "Alaska Pollock". www.fisheries.noaa.gov. National Oceanic and Atmospheric Administration. Retrieved 2023-08-24.
  11. ^ Sugihara, G.; Criddle, K.R.; McQuown, M. (2018). "Comprehensive incentives for reducing Chinook salmon bycatch in the Bering Sea walleye Pollock fishery: Individual tradable encounter credits". Regional Studies in Marine Science. 22: 70–81. doi:10.1016/j.rsma.2018.06.002.
  12. ^ Sugihara, G., and H. Ye. (2009). Cooperative Network Dynamics. Nature 458, 979-980. https://doi.org/10.1038/458979a
  13. ^ Scheffer, M; Bascompte, J; Brock, WA; Brovkin, V; Carpenter, S R; Dakos, V; Held, H; Rietkerk, M; Sugihara, G; et al. (2009). "Early-warning signals for critical transitions". Nature. 461 (7260): 53–59. Bibcode:2009Natur.461...53S. doi:10.1038/nature08227. PMID 19727193. S2CID 4001553.
  14. ^ a b DeAngelis, Donald L.; Yurek, Simeon (2015). "Equation-free modeling unravels the behavior of complex ecological systems". Proceedings of the National Academy of Sciences. 112 (13): 3856–3857. doi:10.1073/pnas.1503154112.
  15. ^ Chang, C; Ushio, M (2017). "Empirical dynamic modeling for beginners". Ecol Res. 32 (6): 785–796. doi:10.1007/s11284-017-1469-9.
  16. ^ Sugihara, G.; May, R. (2012). "Detecting Causality in Complex Ecosystems". Science. 338: 496–500. doi:10.1126/science.1227079.



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