NYU Data Science newsletter – January 21, 2016

GROUP CURATION: N/A

How many calories are on your plate? Engineers at the University of Washington have developed a gizmo that estimates the nutritional value of your meal with the mere snap of a smartphone.

NutriRay3D combines a smartphone app with a laser-mapping add-on: The app identifies what kind of food is in the picture, and the laser-mapper provides an estimate of each food’s volume. Then you get a real-time estimate of the calorie count and nutritional content.

As part of the Data Science Initiative, the Rafik B. Hariri Institute for Computing and Computational Science and Engineering will commence its inaugural Boston University Data Science Day. The event, which will take place Friday from 9 to 4 p.m at the Photonics Center, will aim to foster a deeper knowledge of data science as well as forge connections and collaborations on the topic.

Prakash Ishwar, co-chair of BUDS Day and an electrical and computer engineering professor, said there will be three “thematic-panels” featuring talks by BU faculty, a BU student poster session over lunch period and two 30-minute keynote addresses in the afternoon. A networking reception will also take place at the end of the day.

With U.S. health care costs surpassing $3 trillion a year — an unsustainable 20% of the American economy — we all must find ways to cut costs. At the World Economic Forum in Davos, Dr. John H. Noseworthy, head of the famed Mayo Clinic, explains how the latest advances in computer science offer a promising solution, where better collection and understanding of the billions of data points generated by medical research and treatments can improve patient “outcomes” and lead more effective and affordable health care for millions of people.

I have backed out of a lot of parking spaces in my life, but today is different. As I adjust the mirrors, fasten the safety belt, turn on the ignition, and put the Lexus RX450h SUV in reverse, I feel the scrutiny of my passengers, professionals who may be skeptical of my ability to control the vehicle. I make a couple of turns, and then, on a dashboard display that I’m not allowed to describe to you (trade secret!), something turns green.

That’s my signal. I press a button on the steering column, and a female voice accompanied by an icy synthesizer note?—?the kind of thing you hear when monorail doors are about to close?—?intones the word, “Autodrive.” Something catches in my throat; it may be the closest thing I’ll know to flying the Millennium Falcon when it thrusts into hyperspace. In truth, not much really changes. The Lexus rolls forward and rambles down a street in a neighborhood that is all streets and no buildings or people, a Potemkin village of roadways. There is an intersection ahead with a stop sign. The car stops. My foot has not touched the brake.

I am behind the wheel of a Google self-driving car.

As of Saturday morning January 16, 479 students were signed up for the 481 total seats available in the first regular offering of Foundations of Data Science. We are excited to see this broad, diverse cohort – and at the same time be able to accommodate the high level of student interest in this very new offering. Universities throughout the world are watching the development of this course and the connections to the many disciplines, reflected in the connector courses, that is the essence of data science.

Big Data got a booster shot last November when the National Science Foundation (NSF) announced the Big Data Regional Innovation Hubs program, a three-year, $5-million-plus pilot intended to strengthen the nation’s data ecosystem. Four regional consortiums — Northeast, Midwest, South and West — are coordinating efforts across 50 states and more than 250 organizations that include academia, state agencies, private industry and nonprofits that share common goals.

NSF anticipated the program’s priority areas will include ways that big data can improve health care and management of natural resources, and support environmentalism, precision agriculture, education, personalized medicine, finance and the energy sector.

Western Hub Executive Director Meredith Lee explained that the program is a chance to educate through events like civic hackathons, connect people and groups that don’t typically talk so they can share ideas and resources, encourage innovation, and build new partnerships.

In complexity theory, physicists try to understand economics while sociologists think like biologists. Can they bring us any closer to universal knowledge?

I first met David Chaum in 1994, while writing a story about digital money for Wired Magazine, and he became a key source and subject for my 2001 book, Crypto. He emerged in the news this month as the inventor of PrivaTegrity, a new social media system. His proposal drew a lot of attention and some strong criticism from some sectors of the security community. Since I have always known David as one of the fiercest advocates of privacy I’ve ever met, as well as someone exceedingly skeptical of government encroachment, I encouraged him to explain his ideas here on Backchannel, in his own words.

People Asked About: Deep learning, machine learning, artificial intelligence, academia. Neural language models and their derivatives, recurrent neural networks, convolutional networks, autoencoders, deep generative models.

ICWSM is seeking proposals for advanced tutorials on topics related to the analysis and understanding of social phenomena, particularly as seen on social media. We are looking for contributions from experts in both the social and computational sciences. The tutorials will be an opportunity for cross-disciplinary engagement and a deeper understanding of new tools, techniques, and research methodologies. Each tutorial should provide either an in depth look at an emerging technique or software package or a broad summary of an important direction in the field.

Deadline for submissions is Monday, February 8.

Modern statistical methods allow the analyst to fit and assess models as well as to undertake supervised or unsupervised learning to extract information. Contemporary data science requires tight integration of these statistics, computing, data skills, mathematics and communication. The purpose of this book, which is intended for readers with some background in statistics and modest prior experience in scripting and programming, is to help them develop and practice the appropriate skills to tackle complex data science projects.

At Kaggle, we want to help the world learn from data. This sounds bold and grandiose, but the biggest barriers to this are incredibly simple. It’s tough to access data. It’s tough to understand what’s in the data once you access it. We want to change this. That’s why we’ve created a home for high quality public datasets, Kaggle Datasets.

Many scientists write code as part of their research. Just as experiments are logged in laboratory notebooks, it is important to document the code you use for analysis. However, a few key problems can arise when iteratively developing code that make it difficult to document and track which code version was used to create each result. First, you often need to experiment with new ideas, such as adding new features to a script or increasing the speed of a slow step, but you do not want to risk breaking the currently working code. One often-utilized solution is to make a copy of the script before making new edits. However, this can quickly become a problem because it clutters your file system with uninformative filenames, e.g., analysis.sh, analysis_02.sh, analysis_03.sh, etc. It is difficult to remember the differences between the versions of the files and, more importantly, which version you used to produce specific results, especially if you return to the code months later. Second, you will likely share your code with multiple lab mates or collaborators, and they may have suggestions on how to improve it. If you email the code to multiple people, you will have to manually incorporate all the changes each of them sends.

Fortunately, software engineers have already developed software to manage these issues: version control. A version control system (VCS) allows you to track the iterative changes you make to your code.