Research Center

Knowledge does not arise from the simple accumulation of facts. Rather, it is a complex, dynamic system, and its emergent outcomes - including scientific consensus - are unpredictable.

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People have touted the potential of big data and computation in medicine for what feels like decades, promising more effective and personalized treatments, new research discoveries, and smarter cli

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In recent years, the race to build the fastest computers has been joined by a parallel competition to design the most energy-efficient machines. The colossal data centers supporting cloud computing and web applications consume massive amounts of energy, using electricity to both run and cool their tens of thousands of servers. As engineers look for new CPU designs that reduce energy usage, scientists from Northwestern University and Argonne National Laboratory are seeking an AI-based solution, using the cloud computing testbed Chameleon to reduce power through smarter task traffic.

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Through civic hacking events and open data portals, the Obama administration has embraced the potential of data and programming to improve the performance of government for its citizens. As academia and industry increasingly moves toward using computational techniques to inform policy decisions, these more ambitious efforts have also attracted the attention of the White House. On April 4th, the President's Council of Advisors on Science and Technology (PCAST) convened a panel called “Analytical Techniques to Improve Public Policy Decision-Making” at their regular meeting, inviting CI Senior Fellow Charlie Catlett and three other experts to report on the promise of this young research area.

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While some people still consider social media to be a time-wasting fad, many scientists have started using these services as massive databases, growing every day by millions of new entries from people around the world. These projects have used the platform for predicting stock markets, box office receipts, and reality show results, but DSSG fellows Zahra Ashktorab, Christopher Brown, Manojit Nandi and mentor Aron Culotta worked with the Qatar Computational Research Institute to enlist tweets for a more noble goal: assistance during disasters and emergencies.

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The foundation of the last five decades of biology is an elegant piece of code-breaking: the discovery in the 1960's of how the codons formed by DNA bases are translated into the amino acid pieces

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Where does knowledge come from? How does "certainty" come to be? What role do social, psychological and institutional practices play in these processes?

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As the Data Science for Social Good fellowship enters its final month, many of the projects with nonprofit organizations and government agencies are picking up momentum. At the DSSG website, we're posting regular updates on the fellows' progress: how they determined the right problem to solve, what analytic and software tools they're using to attack those problems, and what they have learned along the way. Some of the articles even offer a glimpse at early results and prototypes developed by the team over the first two months. Here's a sampling of those progress reports.

 

Cook County Land Bank: The Problem

The Cook County Land Bank Authority was recently established earlier this year as a new government agency charged with acquiring and redeveloping vacant and abandoned properties. DSSG fellows are working with The Institute for Housing Studies at DePaul University to developed a tool—a sort of "Trulia for abandoned properties"—that will help the agency determine which properties to purchase in order to produce the greatest benefit for the surrounding community.

Learning a subject well means moving beyond the recitation of facts to a deeper knowledge that can be applied to new problems. Designing computers that can transcend rote calculations to more nuanced understanding has challenged scientists for years. Only in the past decade have researchers’ flexible, evolving algorithms—known as machine learning—matured from theory to everyday practice, underlying search and language-translation websites and the automated trading strategies used by Wall Street firms.

These applications only hint at machine learning’s potential to affect daily life, according to John Lafferty, the Louis Block Professor in Statistics and Computer Science. With his two appointments, Lafferty bridges these disciplines to develop theories and methods that expand the horizon of machine learning to make predictions and extract meaning from data.

“Computer science is becoming more focused on data rather than computation, and modern statistics requires more computational sophistication to work with large data sets,” Lafferty says. “Machine learning draws on and pushes forward both of these disciplines.”