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Archive for the ‘Mathematics’ Category

Photo by Lloyd DeGrane.

Photo by Lloyd DeGrane.

Cities draw their strength from community and diversity, when people from different backgrounds work together in close proximity on big problems. So to unleash the potential of city data, it only makes sense to replicate that mixing bowl effect in the context of research. To formally kick off the new Urban Sciences Research Coordination Network (USRCN), 80 experts representing a broad range of disciplinary knowledge met in downtown Chicago to forge new connections and grand ideas for projects that harness data for the benefit of the modern city.

Computer scientists, mathematicians, public health and education experts, architects, urban planners, social scientists, artists and more gathered inside the ballroom of the School of the Art Institute of Chicago on February 15th with an ambitious goal: form a new interdisciplinary research community for data-driven urban science. Co-hosted by the Urban Center for Computation and Data (UrbanCCD) and the University of Chicago Urban Network and funded by the National Science Foundation, the event was meant as both social mixer and brainstorming session.

“We were asked by the NSF to create this research coordination network as a network of people, not computers,” said Charlie Catlett, director of UrbanCCD. “If you can put teams together that are interdisciplinary and also cut across these experience types, then we can begin to study the city in a way that none of us could do just as individuals or small groups.”

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Marius Stan.Untitled1THE CI’S MOST FAMOUS CAR WASH OWNER

A recurring theme of Breaking Bad is getting out of difficult situations with science. Yet, you still probably wouldn’t expect to run into a character from the hit TV show on the campus of Argonne National Laboratory or University of Chicago. But if you happen to spot a man who looks just like protagonist Walter White’s former boss at his car wash job, no need for a double take — you’re not losing your mind. Marius Stan, a Computation Institute Senior Fellow and Argonne scientist studying computational chemistry and physics, provides the memorable eyebrows and Romanian curses for the role of Bogdan, a character who has appeared in a handful of episodes of the AMC drug-trade drama.

This week in the Chicago Tribune, reporter Ted Gregory profiled Stan and told the story of how he got involved with the show when he lived in Albuquerque, before moving to Chicago. Stan might humbly list “Breaking Bad, Bogdan” below a computational microscope and a book about modeling and simulation in materials science on his CI web page. But his colleague, CI fellow Andrew Siegel, said most people at Argonne find his moonlighting career “extremely cool.”

“Everybody finds it hilarious and great. In science, you’re so uncool, at least in this country, and the world of acting is so opposite of that. It’s a funny convergence of things.”

THE MATH INSIDE ELECTRICAL OUTLETS

Electrical power grids present a complex and challenging mathematical problem, with questions of how to efficiently produce, store and distribute the energy. To study the mathematics behind tomorrow’s power lines, Argonne National Laboratory and Computation Institute scientists formed the Multifaceted Mathematics for Complex Energy Systems Project (M2ACS), which recently received a $17.5 million grant from the Department of Energy. The project will bring together experts from several different areas of mathematical study, including optimization, dynamical systems, uncertainty quantification, random processes, data analysis, discrete mathematics and linear algebra to find new techniques needed to drive next-generation “smart” power grids and other technologies.

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(from Michael Engel and Hans-Rainer Trebin. Structural complexity in monodisperse systems of isotropic particles. Zeitschrift für Kristallographie, 223:721–725, 2008)

A crystal structure equivalent to β-Uranium identified as forming in the LJG system.

Most people think that scientists spend all of their time conducting experiments. But the less glamorous side of science comes after the experiments are done, as scientists laboriously comb through the data their work created. As new technologies make laboratory procedures faster and automatic, more and more of a scientist’s time is spent on the often tedious task of analyzing data. In order to accelerate the speed of discovery, use resources more efficiently and avoid burning out graduate students, new ways of automating data analysis need to be found.

Carolyn Phillips, a Computation Institute staff member and postdoctoral fellow at Argonne National Laboratory, presented one solution to this data analysis traffic jam in her talk at the CI on December 14th.  Phillips works with scientists studying nanoscale self-assembly, the ability of small, simple molecules to form incredibly complex patterns with no external influence. Many researchers in this realm are using computer simulations to understand how self-assembly works and figure out new ways of harnessing it for use in the design of drugs, materials and cleaner energy sources. But these simulations can produce a flood of data, most of which still needs to be sorted manually and analyzed by slow, distractable humans before the next round of simulations can be run – a problem Phillips sought to fix.

“I wanted to find a way to stop having simulations depend on me all the time,” Phillips said. “How do I automate myself; how do I automate my judgment?”

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