University of Vermont

UVM Student Receives Award at iEMSs

Gary JohnsonGary Johnson, Jr., PhD student in Computer Science received an award for “Best Student Paper Prize” at the 6th International Congress on Environmental Modelling and Software (iEMSs) held in Leipzig, Germany July 1-5, 2012 for his paper entitled, “Modelling Ecosystem Service Flows under Uncertainty with Stochastic SPAN”.  

Johnson graduated summa cum laude from Marlboro College in 2005 with a B.S. in Computer Science/Geoinformatics.  At UVM, he is one of the primary technical developers of the NSF-funded ARIES (Artificial Intelligence for Ecosystem Services) project. His faculty advisors are Drs. Robert Snapp, Jon Erickson, and Austin Troy.

The 2012 IEMSs theme was: “Managing Resources of a Limited Plant: Pathways and Visions under Uncertainty.”  The iEMSs goal is to enhance understanding of environmental processes and decision making by fostering the discussion and interchange of challenges, solutions, ideas, new methods and techniques, and future research lines in environmental modelling and software.  The meetings of iEMSs are joint events integrating different research groups, such as BESAI, ERCIM, ISEM, ISESS, MODSS, and TIAS.

Session participants presented innovative research work within nine thematic streams: Environmental Information; Human Health and Environmental Risks, Mitigation of and Adaption to Climate Change, Model Development, Analysis and Application: Methodological Aspects; Participatory Modelling and Stakeholder Involvement; Resource Management and Sustainability; Knowledge, Data and Semantic Processing for Environmental Research; Socio-Environmental Systems, and Issues in Water Resources Management.

Johnson’s research interests include  ecosystem service modelling, data and model scaling, neural and evolutionary computation, machine learning, stochastic processes, knowledge representation and reasoning under uncertainty, decision and game theory, statistical modelling, image analysis, functional programming, spatial statistics, and pattern classification.

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