NYU Data Science newsletter – August 31, 2015

GROUP CURATION: N/A

The SNAP library is being actively developed since 2004 and is organically growing as a result of our research pursuits in analysis of large social and information networks. Largest network we analyzed so far using the library was the Microsoft Instant Messenger network from 2006 with 240 million nodes and 1.3 billion edges.

Since 2009, Google has been overseeing the community-led development of Go — a programming language aimed at helping web developers build apps at Google’s scale and Google’s speed.

Go has won its fair share of fans in programmer-land, not least because it provides a viable alternative to Oracle’s Java, which has ruled the world of computer programming for the last two decades. Google’s been using Go internally to power things like its download servers, where you grab stuff like the Chrome install files.

Today, Go hits version 1.5, marking a huge milestone for the project as it ditches the last vestiges of the C programming language, according to the project’s blog.

Throughout all areas of data science, researchers are confronted with increasingly large volumes of data. In many fields, this increase is exponential in nature, outpacing Moore’s and Kryder’s laws on the respective doublings of transistors on a chip and long-term data storage density (Kahn, 2011). As such, the challenges posed by the massive influx of data cannot be solved by waiting for faster and larger capacity computers but, instead, require the development of data structures and representations that exploit the structure of the dataset.

Facebook unveiled ‘M’ its virtual assistant that lives in Messenger this week, which is a conversational assistant that can do anything from tell you jokes to ordering concert tickets.

M is still in beta and is not available for everyone yet but Jackson Fall, head of product at Wealthcoin, already has access so helped us take it for an early spin.

Plenty of people have been working on electronic consent forms over the years, taking on pieces of the problem, like getting digital signatures. Nothing has caught on in a big way.

[John] Wilbanks and the Sage crew are building on the lessons learned. They appear to have taken a small but meaningful step through their partnership with Apple and its ResearchKit. This spring, like with many things Apple does, it debuted the new platform to much fanfare. Sage immediately saw thousands of people sign up for an observational study of Parkinson’s patients, one of five ongoing demo projects.

It turned out to be pretty easy for Yaniv Erlich to identify people who had donated their genomic data to science, even despite researchers’ attempts to make that data anonymous. Erlich, a former computer hacker hired by businesses to test the security of computer systems, only needed his tailor-made computer program and an Internet connection to access publicly available databases.

Erlich, though, had no nefarious intentions. As a fellow at the Whitehead Institute for Biomedical Research, his was an academic exercise to show that such privacy systems were flawed. To keep others from repeating his work, however, Erlich omitted some of the necessary steps from his group’s published paper (1). Although this was an unusual omission for a scientific paper, Erlich was concerned that someone could repeat his work and reveal the individuals’ identities, and expose their genomic data—potentially affecting relationships, employability, and insurability.

THE internet runs on opinions. If you want to eat out, visit a faraway city or just get a new toothbrush, there are thousands of online reviews to help you make choices. Services like Yelp, Airbnb and Amazon rely on this electronic word of mouth to keep users coming back.

But savvy consumers know that user-generated reviews aren’t guaranteed to be credible. Bad comments about a hotel might have been penned by someone with an axe to grind; a rave restaurant review could be by the chef’s mother. That’s led some tech companies to ask: how can we make online opinons more reliable?

For more than a decade, gene sequencers have been improving more rapidly than the computers required to make sense of their outputs. Searching for DNA sequences in existing genomic databases can already take hours, and the problem is likely to get worse.

Recently, Bonnie Berger’s group at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has been investigating techniques to make biological and chemical data easier to analyze by, in some sense, compressing it.

While using the Internet and mobile devices, people create data, whether intentionally or unintentionally, through their interaction with messaging services, websites and other applications and devices. This means that experiments with heretofore unprecedented populations can be performed in a variety of topics. Our workshop will focus on observational studies which arise from these interactions and data, with a focus on experiments that can indicate causal inferences.

Deadline for Submissions: Friday, October 9

Cushman & Wakefield — a renowned international commercial real estate services firm — announced in August its exciting decision to serve as exclusive real estate sponsor for NYU’s incubator program.

With this sponsorship comes not only a promise of financial support, but also expert guidance in the form of leasing advice, corporate finance services and project management.