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Rescooped by Mark Corbett Wilson from Talks
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What Big Data Means For Social Science

We've known big data has had big impacts in business, and in lots of prediction tasks. I want to understand, what does big data mean for what we do for science? Specifically, I want to think about the following context:  You have a scientist who has a hypothesis that they would like to test, and I want to think about how the testing of that hypothesis might change as data gets bigger and bigger. So that's going to be the rule of the game. Scientists start with a hypothesis and they want to test it; what's going to happen?

 


Via Alessandro Cerboni, NESS, Complexity Digest
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Rescooped by Mark Corbett Wilson from CxConferences
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17th International Workshop on Nature Inspired Distributed Computing (NIDISC 2014)

17th International Workshop on Nature Inspired Distributed Computing (NIDISC 2014) | Complexity | Scoop.it

NIDISC 2014 is the 17th International Workshop on Nature Inspired Distributed Computing

 

Held in conjunction with The 28th IEEE/ACM International Parallel and Distributed Processing (IPDPS 2014)

 

May 19-23, 2014
Phoenix, Arizona

 

Techniques based on metaheuristics and nature-inspired paradigms can provide efficient solutions to a wide variety of problems. Moreover, parallel and distributed metaheuristics can be used to provide more powerful problem solving environments in a variety of fields, ranging, for example, from finance to bio- and health-informatics.

This workshop seeks to provide an opportunity for researchers to explore the connection between metaheuristics and the development of solutions to problems that arise in operations research, parallel computing, telecommunications, and many others. Topics of interest include, but are not limited to:

Nature-inspired methods (e.g. ant colonies, GAs, cellular automata, DNA and molecular computing, local search, etc) for problem solving environments.Parallel and distributed metaheuristics techniques (algorithms, technologies and tools).Applications combining traditional parallel and distributed computing and optimization techniques as well as theoretical issues (convergence, complexity, etc).Other algorithms and applications relating the above mentioned research areas.

 

Submission Deadline - December 16, 2013
Notification of Acceptance - January 27, 2014
Final Copy Due - March 14, 2014
Workshop - May 19-23, 2014

 

http://nidisc2014.gforge.uni.lu/

 


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Rescooped by Mark Corbett Wilson from CxConferences
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NECSI Winter School 2014

NECSI Winter School
January 6 - 17, 2014

Jan. 6-10 CX201: Complex Physical, Biological & Social Systems

Jan. 12 CX102: Computer Programming and Complex Systems

Jan. 13-17 CX202: Complex Systems Modeling and Networks

Location: MIT, Cambridge, MA

 

These courses are intended for faculty, graduate students, post-doctoral fellows, professionals and others who would like to gain an understanding of the fundamentals of complex systems for application to research in their respective fields, or as a basis for pursuing complex systems research. The winter school offers two intensive week-long courses. The courses consist of lectures, discussions, and supervised group projects. Though the second week builds on material covered in the previous week, CX201 is not a prerequisite for CX202. You may register for either or both weeks. If desired, arrangements for credit at a home institution may be made in advance.

 

See course descriptions: http://www.necsi.edu/education/school.html

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