NYU Data Science newsletter – June 3, 2015

GROUP CURATION: N/A

… Robots learning from robots

Because of the breadth and availability of its content, Wikipedia has been widely used as a reference dataset for research in machine learning and for tech demos. However, Wikipedia has some serious problems that are not apparent from our familiarity with it as a resource for human beings.

We have just added a new short lesson called Working With Data on the Web to our repertoire. If you would like to help us improve it, please fork it on GitHub and send us comments or pull requests.

On this blog, I’ve had a long running investigation/demonstration of how to make a “embarrassingly-parallel” but computationally intractable (on commodity hardware, at least) R problem more performant by using parallel computation and Rcpp.

The example problem is to find the mean distance between every airport in the United States. This silly example was chosen because it exhibits polynomial growth in running time as a function of the number of airports and, thus, quickly becomes intractable without sampling. It is also easy to parallelize.

The College of Media, Communication and Information at the University of Colorado Boulder is proud to introduce its newest department, the Department of Information Science, and to announce the appointment of six founding faculty members who will begin at CU-Boulder in fall 2015: Jed Brubaker, Casey Fiesler, Michael J. Paul, Danielle Albers Szafir, Amy Voida and Stephen Voida. … We welcome your interest and participation as we grow and develop our new programs. The Department will begin offering courses for BS and PhD programs in information science in fall 2016. An MS program is currently expected to launch in fall 2018.

The application of big and new data to social science questions requires partnerships — not only between humans and machine, but also among different disciplines.

That need for collaboration, on both fronts, was the theme behind Kellogg’s inaugural Computational Social Science Summit (CSSS), held May 15-17 on Northwestern’s Evanston campus.

The three-day conference brought together participants from the social, computer and natural sciences to explore how machine learning can find new insight within the realm of social science.

The eScience Institute is pleased to announce the four projects chosen for our inaugural Data Science for Social Good (DSSG) summer program. Modeled after similar programs at the University of Chicago and Georgia Tech, with elements from our own Data Science Incubator, the goal of the DSSG program is to enable new insight by bringing together data and domain scientists to work on focused, collaborative projects that are designed to impact public policy for social benefit.

The theme for this year’s DSSG projects was Urban Science. We encouraged project proposals that involved analysis and visualization and/or software engineering of data from urban environments across topic areas including public health, sustainable urban planning, crime prevention, education, transportation, and social justice.

Bayes Impact co-founder Paul Duan discusses the challenges of running the data-driven nonprofit startup, and advice for students interested in tech for good.

Bayes Impact is a nonprofit startup under Y Combinator that utilizes data science to create new technological solutions for problems worldwide. Paul Duan, co-founder and president of Bayes Impact, joined Blueprint for a fireside chat on April 22. Here are some of the insights and experiences he shared!

Sometimes, the important part of a story is its simplicity.

When we at The Upshot were digging through the results of a large recent study on upward mobility, Amanda Cox noticed a striking relationship. The odds that a child would attend college by age 21 just kept rising with that child’s family income. There were no plateaus, as seemed intuitive. Upper-middle-class children — those in families making several hundred thousand dollars a year — were less likely to go to college than children from even more affluent families.

At 12, Stephen Wolfram started writing a dictionary about physics, and the California Institute of Technology awarded him a Ph.D. at age 20. The brilliant, enigmatic creator of Mathematica and founder of Wolfram Alpha now runs a massive company remotely, spending his days on the phone and with screen share as he works through problem after problem with his staff. His latest development is the Image Identification project, which will enable your computer to tell you what picture it’s looking at. He talked to Pacific Standard about computing the world’s knowledge, how his kids got him to start traveling, and why he’s not scared of artificial intelligence.

The John D. and Catherine T. MacArthur Foundation has granted funding to a trio of New York University engineering and law professors who are developing cutting-edge visualization tools that will enable human rights advocates to convey complex information and vividly display their view of a better world.

Although the human rights field is increasingly using data to describe such phenomena as drone attacks, conflict-related deaths, and violations of economic and social rights, very few organizations have the concrete knowledge of data science and the resources needed to create and incorporate effective visualization tools like interactive maps and sophisticated infographics. They lag media in employing these tools—all the more surprising because these graphic elements affect public policy.

The three NYU researchers who are leading the team to rectify that situation are Assistant Professor of Computer Science and Engineering Enrico Bertini and Associate Professor of Technology Management and Innovation Oded Nov, both of the Polytechnic School of Engineering; and Professor of Clinical Law Margaret Satterthwaite, who also serves as the faculty director for NYU’s Center for Human Rights and Global Justice.