NYU Data Science newsletter – March 8, 2016

GROUP CURATION: N/A

… Can you give us a little bit of background on the work you’re doing at Alluvium?

What we’re doing is building human operator-centered analytic tools for what is being called the ‘Industrial Internet of Things’. The motivation at Alluvium is to build in various parts of different industries many products that perform inference, or make choices, to enhance the user experience based on data. Our engineers and data scientists are thinking about how to solve problems that will arise over the course of the next decade or more as new types of instrumentation arise and data availability improves.

As big data solutions and the businesses behind them have staked their claim within the healthcare arena, a Bethesda, Md.-based company [Precision for Medicine] has taken precise aim at capitalizing on the explosion of immunotherapies within the oncology space.

Richard Socher appeared nervous as he waited for his artificial intelligence program to answer a simple question: “Is the tennis player wearing a cap?”
From Our Advertisers

The word “processing” lingered on his laptop’s display for what felt like an eternity. Then the program offered the answer a human might have given instantly: “Yes.”

Mr. Socher, who clenched his fist to celebrate his small victory, is the founder of one of a torrent of Silicon Valley start-ups intent on pushing variations of a new generation of pattern recognition software, which, when combined with increasingly vast sets of data, is revitalizing the field of artificial intelligence.

From searching on Google to real-time translation, millions of people use deep learning every day, mostly without knowing it. It’s a form of artificial intelligence designed to mimic the human brain. Geoffrey Hinton is a professor in the department of computer science at the University of Toronto. His work on deep learning has been snapped up by Google and is now being used to power its search engine. He joins The Agenda to discuss deep learning and the future of artificial intelligence.

A terrorist attack might seem like one of the least predictable of events. Terrorists work in small, isolated cells, often using simple weapons and striking at random. Indeed, the element of unpredictability is part of what makes terrorists so scary – you never know when or where they will strike.

However, new research shows that terror attacks may not be as unpredictable as people think. A paper by Stephen Tench and Hannah Fry, mathematicians at the University College London, and Paul Gill, a security and crime expert, shows that terrorist attacks often follow a general pattern that can be modeled and predicted using math.

Imagine you’re a patient who has just started taking two new drugs: one for high cholesterol and the other for depression. Each drug has been tested, evaluated for side effects and approved by the FDA, so your physician feels confident that you’ll be fine taking these medications. Independently at least. What’s less clear is how these two drugs could interact when taken together, or with any other medication you may already be taking.

This is the scenario that Russ Altman, MD, PhD, a professor of bioengineering, genetics, medicine and computer science at Stanford, invited the audience to contemplate at TEDMED 2015.

To begin to assess the research opportunities provided by today’s web-connected world and advanced technologies, DARPA today launched its Next Generation Social Science (NGS2) program. The program aims to build and evaluate new methods and tools to advance rigorous, reproducible social science studies at scales necessary to develop and validate causal models of human social behaviors. The program will draw upon and build across a wide array of disciplines—including social sciences like sociology, economics, political science, anthropology, and psychology, as well as information and computer sciences, physics, biology and math.

At the White House Precision Medicine Initiative Summit on February 25, 2016, the New York Genome Center and IBM announced that they are collaborating to create a comprehensive and open repository of genetic data to accelerate cancer research and scale access to precision medicine using cognitive insights from IBM Watson. Analyzing this data alongside the medical community’s growing knowledge about cancer could help accelerate the ability of doctors to deliver personalized treatment to individual patients.

IBM and New York Genome Center are working together to build the capacity to house the contributed data, train Watson’s cognitive computing capabilities for genomic analysis and enable the Center’s member institutions and other research collaborators to sequence and analyze DNA and RNA from patients’ tumors.

2016 Sentiment Analysis Symposium will examine the business value of sentiment, opinion, and emotion in our big data world.

We’re inviting talks that focus on customer experience, brand strategy, market research, media & publishing, social insights, healthcare, and financial markets. On the tech side, show off what you know about natural language processing, machine learning, speech and emotion analytics, and the data economy.

Deadline to submit proposals is Tuesday, March 15.

The Moore-Sloan Data Science Environment at NYU is announcing a unique funding opportunity, open to all NYU faculty, that aims to bring together data scientists and domain scientists to foster collaborations and generate new ideas. For details, please see the Seed Grant Announcement. If you are interested in applying for the Seed Grant, you must first submit an online letter of interest by March 25, 2016. You must then attend the “open dating session” to pitch your ideas to the other attendees. The open dating session is scheduled from 4pm to 6pm on April 4, 2016 and the location is to be announced. The PIs must submit a formal proposal (1-2 pages) within two weeks of the “open dating” session. The proposals will be reviewed by the MSDSE Methods Working Group based on the project impact, innovation and scientific merits of the proposal. The results will be announced by May 13, 2016.

Deadline for submitting this online letter of interest is Friday, March 25.

Computer Science research from Adam Wentz and Alex Champandard (coincidentally, not together) extends neural network style transfer method allowing to dictate how image sections should be stylisticly composed and rendered (in two different ways).

What does a day in the data science life look like? Here is a very helpful framework that is both a way to understand what data scientists do, and a cheat sheet to break down any data science problem.