Data Science newsletter – April 1, 2017

Newsletter features journalism, research papers, events, tools/software, and jobs for April 1, 2017

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Data Science News



How I Let Disney Track My Every Move

Gizmodo, Adam Clark Estes


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Technology has changed the Disney experience—and not necessarily in a bad way. These days, you can get something called a MagicBand, a radio-powered bracelet that will open your hotel room door at the Disney resorts, let you into the parks, let you get onto rides more quickly, and even pay for your breakfast at Gaston’s Tavern. It’s also communicating with beacons hidden throughout the park to let Disney know what you’re doing and where you’re going.

Disney first introduced the technology in 2013 and recently updated it, but I just encountered the band firsthand on my vacation. I still can’t stop thinking about it. The very notion of wearing a tracking bracelet freaks me out. (It’s weird enough that you have to supply your fingerprint at the front gate of Disney World, as well as other theme parks, these days.) I realize that this is something I signed up for—Disney will still let you use paper tickets and avoid MagicBands if you like—but I arrived at the park pretty clueless about the extent to which Disney would be tracking my every move. It’s kind of like signing up for Facebook with the hope that you can connect with far off friends, only to realize several years later that the social network has been gobbling up your online activity in order to sell ads. You agreed to this deal. Nevertheless, you probably didn’t comprehend every detail buried in the fine print.


Designing the User Experience of Machine Learning Systems

Mike Kuniavsky, Elizabeth Churchill, Molly Wright Steenson


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Papers from the 2017 AAAI Spring Symposium


Robotics reboot

UW Mechanical Engineering


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Since its earliest days, the team has experienced rapid growth. Now with a core membership of about 75 students, team leaders have decided to turn their attention inward.

According to Estroff, the [Husky Robotics] team determined it needed a more focused plan for member recruitment and retention, as well as training, skills building and knowledge transfer, so leadership has been working to establish a secure infrastructure for the team. In the last year, they have restructured workflow and written a new constitution to define roles and document policies, procedures and team history. They have adapted project management skills and practices that team members have learned through internships, and they have been working to expand their profile on campus, by participating in events like Engineering Discovery Days and launching a new website. And a few months ago they began working with a new faculty adviser, ME assistant professor Sawyer Fuller.


Inside IC3: How Cornell is Advancing the Science of Bitcoin

CoinDesk, Alyssa Hertig


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The whirring noise is so loud it drowns out Cornell PhD candidate Adem Efe Gencer as he explains that inside the mundane set of racks is Cornell’s ambitious attempt to model the global bitcoin network – all in the name of science.

The servers, he notes, make up about half of a bitcoin testbed that comprises more than 1,200 nodes. More are in the basement below.


RightHand Robotics Gets $8M Led by Andy Rubin: Warehouse Robots

BostInno, Dylan Martin


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RightHand’s new robot, called the RightPick, is a combined hardware and software system the company claims can pick up and sort small objects, typically five pounds or less, anywhere from 500 to 1,000 times an hour. The system uses machine learning software and sensors to figure out how to handle various items on the fly.


At BlackRock, Machines Are Rising Over Managers to Pick Stocks

The New York Times, Dealbook blog, Landon Thomas Jr.


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Score one for the machines.

The largest fund company in the world, BlackRock, has faced a thorny challenge since it acquired the exchange-traded-fund business from Barclays in 2009.

These low cost, computer-driven funds have exploded in growth, leaving in the dust the stock pickers who had spurred an earlier expansion for the firm. The rise of passive investing — exchange-traded funds, index funds and the like — has revolutionized the investment world, providing Main Street investors with greater opportunities at lower fees while putting pressure on even Wall Street’s biggest money managers.

Now, after years of deliberations, Laurence D. Fink, a founder and chief executive of BlackRock, has cast his lot with the machines.

On Tuesday, BlackRock laid out an ambitious plan to consolidate a large number of actively managed mutual funds with peers that rely more on algorithms and models to pick stocks.


Machine Learning Lets Scientists Reverse-engineer Cellular Control Networks

Texas Advanced Computing Center


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“We, as a community are drowning in quantitative data coming from functional experiments,” says Michael Levin, professor of biology at Tufts University and director of the Allen Discovery Center there. “Extracting a deep understanding of what’s going on in the system from the data in order to do something biomedically helpful is getting harder and harder.”

Working with Maria Lobikin, a Ph.D. student in his lab, and Daniel Lobo, a former post-doc and now assistant professor of biology and computer science at the University of Maryland, Baltimore County (UMBC), Levin is using machine learning to uncover the cellular control networks that determine how organisms develop, and to design methods to disrupt them. The work paves the way for computationally-designed cancer treatments and regenerative medicine.

“In the end, the value of machine learning platforms is in whether they can get us to new capabilities, whether for regenerative medicine or other therapeutic approaches,” Levin says.


Who Owns Your Face?

The Atlantic, Adrienne LaFrance


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It takes a feast of facial imagery to teach a machine how to recognize an individual person.

This is why computer scientists so often use the faces of Hollywood celebrities in their research. Tom Hanks, for example, is in so many publicly available photographs that it’s fairly easy to build a Hanks database for algorithm-training purposes.

Depending on a researcher’s needs, there are many other available databases of human faces—some featuring tens of thousands of images. These collections of faces draw from public records like mugshots, surveillance footage, news photos, Google images, and university studies.


Release Of Possible Topics For 2020 Census Raises Concerns

NPR, Morning Edition, Hansi Lo Wang


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The U.S. Census Bureau is set to release proposed topics for questions in the 2020 census. Some demographers are worried that the census will become too politicized under the Trump administration. [audio, 2:55]


University Data Science News

A statistician (Francesca Dominici) and computer scientist (David Parkes) will co-lead Harvard’s new Data Science Initiative. This new initiative will welcome seven postdocs, make internal research grants available, and launch three new masters programs. A member of the steering committee, Gary King, has been working on text analysis for over a decade, publishing a new algorithm last week.

Want to launch a start-up and keep your job as a professor or postdoc? You definitely want to know how conflicts of interest are handled to avoid:

1) accidentally preventing your students from publishing on their research;

2) having to ask a journal to print a correction;

3) giving yourself a CEO, CIO, or Chief Scientist title. Don’t be a chief.

4) getting in hot water with your university’s tech transfer office.

Howard University is opening an outpost on Google’s Mountain View campus to try to literally close the gap between talented minority students and Google employment.

Mathematical biologist Samuel Scarpino and network scientist Giovanni Petri dropped significant recent work on arXiv,
[1703.07317] On the predictability of infectious disease outbreaks.

The University of California San Diego just received a mega-gift of $75m from Taner Halicioglu (early Facebook employee) for data science. UCSD’s enrollment has been soaring and instead of relying on public support, the school is looking for a total of $2bn in private donations to meet growing demand for education.



Two different teams of researchers are competing to get rid of those aggravating inexplicable traffic jams. MIT researchers are focusing on network traffic jams; Nanyang Technological Institute researchers are focusing on vehicular traffic jams.

Eitan Adam Pechenick, Christopher M. Danforth, Peter Sheridan Dodds published a paper pointing out why the Google Books corpus is not a great indicator of sociocultural linguistic relevance, along with some work arounds.


Harvard scientists help develop algorithm that predicts social cooperation

Harvard Gazette


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Large social networks foster connections by erasing national, geographic, and even linguistic barriers. But when it comes to fostering cooperation, global connectivity leaves something to be desired, new research says.

Working with colleagues at Emmanuel College, Harvard scientists have developed an algorithm that predicts whether a social structure is likely to favor cooperation, and the findings suggest that strong pairwise relationships — not loose networks scattered across the globe — are the most conducive to cooperation. The study is described in a March 29 paper in Nature.

“What we are able to do is calculate the critical benefit-to-cost ratio for cooperation to thrive on any fixed population structure,” said senior author Martin Nowak, a professor of mathematics and of biology and director of the Program for Evolutionary Dynamics. “And what we find is truly interesting. We can take any graph or social network, and if it has strong pairwise ties, that is what is most conducive for cooperation. This is a mathematical argument for stable families or for stable friendships.”


Computer-Assisted Keyword and Document Set Discovery from Unstructured Text

Gary King


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The (unheralded) first step in many applications of automated text analysis involves selecting keywords to choose documents from a large text corpus for further study. Although all substantive results depend on this choice, researchers usually pick keywords in ad hoc ways that are far from optimal and usually biased. Paradoxically, this often means that the validity of the most sophisticated text analysis methods depend in practice on the inadequate keyword counting or matching methods they are designed to replace. Improved methods of keyword selection would also be valuable in many other areas, such as following conversations that rapidly innovate language to evade authorities, seek political advantage, or express creativity; generic web searching; eDiscovery; look-alike modeling; intelligence analysis; and sentiment and topic analysis. We develop a computer-assisted (as opposed to fully automated) statistical approach that suggests keywords from available text without needing structured data as inputs. This framing poses the statistical problem in a new way, which leads to a widely applicable algorithm. Our specific approach is based on training classifiers, extracting information from (rather than correcting) their mistakes, and summarizing results with Boolean search strings. We illustrate how the technique works with analyses of English texts about the Boston Marathon Bombings, Chinese social media posts designed to evade censorship, among others.


Ben Levine: A City-University Partnership Upon a Hill

MetroLab Network


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I had the pleasure of visiting Boston and participating in the Boston Area Research Initiative’s (BARI) recent conference Data-Driven Research, Policy, & Practice: Lessons from Boston, for Boston.

It’s no secret that Boston’s terrific universities have been a major factor in driving economic growth in the region; nor is it a secret that Boston has a thriving civic technology community, beginning with the leadership of Mayor Marty Walsh and his brilliant team, including Nigel Jacob and Jascha Franklin-Hodge.

So what happens when you combine the power of Boston’s universities with the challenges and opportunities facing the region? BARI’s conference revealed the work already underway and provided a glimpse into the future of city/university collaboration.

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