NYU Data Science newsletter – November 20, 2015

GROUP CURATION: N/A

rOpenSci, whose mission is to develop and maintain sustainable software tools that allow researchers to access, visualize, document, and publish open data on the Web, is pleased to announce that it has been awarded a grant of nearly $2.9 million over three years from The Leona M. and Harry B. Helmsley Charitable Trust. The grant, which was awarded through the Trust’s Biomedical Research Infrastructure Program, will be used to expand rOpenSci’s mission of developing tools and community around open data and reproducible research practices.

… When I got the chance, through WiMLDS, to attend the PyData conference in NYC last week, I jumped at the opportunity to connect with the group of people that were actively working on the things that I found useful, and also beautiful.

The conference started, fittingly, with a talk titled Python as the Zen of Data Science by Travis Oliphant. Oliphant is the creator of NumPy and SciPy and also the founder of NumFOCUS, a non profit that promotes and supports much of the open source scientific software in Python. He talked about how pythonic data analysis really “fits the brain” of the data scientist, rather than the other way around, and, honestly, I couldn’t agree more.

Monsanto CEO Hugh Grant outlined the company’s growth plans at Monsanto’s investor day gathering. Those plans focus on data science, the “glue that holds the pieces together,” Grant said.

Scientific advances typically produce massive amounts of data, which is, of course, a good thing. But when many of these datasets are at multiple locations, instead of all in one place, it becomes difficult and costly for researchers to extract meaningful information from them.

So the question becomes: “How do we learn from these datasets if they cannot be shared or placed in a central location?” says Trilce Estrada-Piedra.

Estrada-Piedra, an assistant professor of computer sciences at the University of New Mexico (UNM) is working to find the solution.

Over half the world’s population live in cities and urban areas, and over the next thirty years, 2 billion more people are expected to become urban residents. Cities are thinking carefully about the challenges associated with such rapid growth – like avoiding over-stressed public transit infrastructure and reducing traffic congestion. We’re interested in these questions too: we’ve been helping people navigate urban areas and route around traffic jams for many years.

Toronto’s Meta announced today that they have raised a $6 million financing round led by Rho Canada Ventures and including Western Technology Investment and iGan Partners. … Meta recently launched its AI-powered science information platform, an end-to-end service that enables researchers and scientific industries to navigate the entirety of scientific information (25 million papers with 4,000 new ones published daily) and instantly identify key insights that would normally take days or even weeks to find.

The Gordon and Betty Moore Foundation has awarded $13.5 million to Stanford University for an international effort, including key contributions from the Department of Energy’s SLAC National Accelerator Laboratory, to build a working particle accelerator the size of a shoebox. It’s based on an innovative technology known as “accelerator on a chip.”

This novel technique, which uses laser light to propel electrons through a series of artfully crafted glass chips, has the potential to revolutionize science, medicine and other fields by dramatically shrinking the size and cost of particle accelerators.

… The deluge of transport timetables, crime logs, pollution readings, property-tax records and the like has been a boon. It has allowed governments to serve citizens better, powered innovative startups and improved people’s lives. But it is not yet clear whether it will effect a transformation. For that to happen, the enthusiasm that drove so much information online has to mature into cool-headed pragmatism.

The trite and cliché line about Internet business models over the years has become, “if you are not paying, you are the product.”

On a base level, being the product is not necessarily a bad thing. People get services and access to the entire world of information and communication without having to spend a dime. As long as they are willing to see and be targeted by ads (in a respectful manner, hopefully), people can derive tremendous benefit from “being the product.”

The problem with people as the product is that it is inherently dehumanizing. People are taken together as an aggregate sum and lumped into PowerPoint and distilled down to acronyms based on one key word …

User.

Please join me in inspiring others to study Machine Learning. Regardless of where you learned Machine Learning, if it has had an impact on you or your work, please share your story on Facebook or Twitter in a short written or video post. I will invite the people who shared the 5 most inspirational stories to join me in a conversation on Google Hangout about the future of machine learning. [video, 1:13]

The deadline to post submissions is Sunday, November 29.

The National Institute on Drug Abuse (NIDA), part of the National Institutes of Health, announces a Challenge to develop novel mobile applications (apps) for future addiction research explicitly created on Apple Inc.’s ResearchKit framework. The apps should be designed to be used in future clinical research studies with human subjects to answer important scientific questions about the paths people take to avoid or to succumb to drugs and to improve the scientific understanding of drug use and addiction. NIDA is also interested in further understanding abstinence and wellness as it relates to drug addictions. NIDA will award up to $100k to the Challenge winners.

The deadline for submissions is Friday, April 29, 2016.

An analytical model that finds mobile device users based on their shared interests is a Holy Grail for e-marketers, because relevant, targeted campaigns have long been proven to be the most effective strategy with the highest return. Now NYU Stern Professor Foster Provost and co-authors have devised a way to connect the same and similar mobile users based on analyzing location visitation data – without compromising users’ privacy.