NYU Data Science newsletter – February 18, 2016

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Monitoring and modeling biomedical, health care and wellness data from individuals and converging data on a population scale have tremendous potential to improve understanding of the transition to the healthy state of human physiology to disease setting. Wellness monitoring devices and companion software applications capable of generating alerts and sharing data with health care providers or social networks are now available. The accessibility and clinical utility of such data for disease or wellness research are currently limited. Designing methods for streaming data capture, real-time data aggregation, machine learning, predictive analytics and visualization solutions to integrate wellness or health monitoring data elements with the electronic medical records (EMRs) maintained by health care providers permits better utilization. Integration of population-scale biomedical, health care and wellness data would help to stratify patients for active health management and to understand clinically asymptomatic patients and underlying illness trajectories. In this article, we discuss various health-monitoring devices, their ability to capture the unique state of health represented in a patient and their application in individualized diagnostics, prognosis, clinical or wellness intervention. We also discuss examples of translational bioinformatics approaches to integrating patient-generated data with existing EMRs, personal health records, patient portals and clinical data repositories. Briefly, translational bioinformatics methods, tools and resources are at the center of these advances in implementing real-time biomedical and health care analytics in the clinical setting. Furthermore, these advances are poised to play a significant role in clinical decision-making and implementation of data-driven medicine and wellness care.

rOpenSci is a developer collective originally formed in 2011 by graduate students and post-docs from ecology and evolutionary biology to collaborate on building software tools to facilitate a more open and synthetic approach in the face of transformative rise of large and heterogeneous data. Born on the internet (the collective only began through chance discussions over social media), we have grown into a widely recognized effort that supports an ecosystem of some 45 software packages, engages scores of collaborators, has taught dozens of workshops around the world, and has secured over $480,000 in grant support. As young scientists working in an academic context largely without direct support for our efforts, we have first hand experience with most of the the technical and social challenges WSSSPE seeks to address. In this paper we provide an experience report which describes our approach and success in building an effective and diverse community.

Hi, I’m Noah. I work at Basecamp. Sometimes I’m called a “data scientist.” Mostly, I just do arithmetic, and I’m ok with that.

A selection of the presentations and talks that took place at the Deep Learning Summit in San Francisco on 28-29 January 2016: https://www.re-work.co/events/deep-learning-sanfran-2016.

Azer Bestavros, founding director of the Rafik B. Hariri Institute for Computing and Computational Science & Engineering, was practically giddy. It was the first BU Data Science (BUDS) Day and the Photonics Center ninth-floor conference room, where the institute was hosting the event, was standing room only.

“I thought there might be 80, 90 registrants,” said Bestavros, a College of Arts & Sciences professor of computer science and head of BU’s Data Science Initiative, welcoming the participants with—what else—data. “They told me there were 262. I was shocked—really?”

… Machine Learning-as-a-Service comes in with a promise to simplify and democratize Machine Learning: reap the benefits of Machine Learning within a short timeframe while keeping costs low.

Several key players have entered that field: Google Predictive Analytics, Microsoft Azure Machine Learning, IBM Watson, Big ML and many others. Some offer a simplified Prediction Analytics service while others offer a more specialized interface and data science services beyond prediction.

One relatively new entrant is AWS with its Amazon Machine Learning service.

More people now get their political news from social media than network news, radio or newspapers, a new report by the Pew Research Center finds. But how that’s shaped the presidential primaries so far is anyone’s guess.

Greg Wawro, a political scientist at Columbia University, organized a hackathon last month to explore the question. Five teams of students sifted through 60,000 records tied to the presidential primaries, ranging from candidate press releases to posts on Twitter, Facebook and You Tube, to see how candidates used the medium to their advantage.

In the past couple of years imbroglios erupted on college campuses across the U.S. over trigger warnings (for example, alerting students to scenes of abuse and violence in The Great Gatsby before assigning it), microaggressions (saying “I believe the most qualified person should get the job”), cultural appropriation (a white woman wearing her hair in cornrows), speaker disinvitations (Brandeis University canceling plans to award Ayaan Hirsi Ali an honorary degree because of her criticism of Islam’s treatment of women), safe spaces (such as rooms where students can go after a talk that has upset them), and social justice advocates competing to signal their moral outrage over such issues as Halloween costumes (last year at Yale University). Why such unrest in the most liberal institutions in the country?

Although there are many proximate causes, there is but one ultimate cause—lack of political diversity to provide checks on protests going too far. A 2014 study conducted by the University of California, Los Angeles, Higher Education Research Institute found that 59.8 percent of all undergraduate faculty nationwide identify as far left or liberal, compared with only 12.8 percent as far right or conservative. The asymmetry is much worse in the social sciences.

The Stanford Artificial Intelligence Laboratory is opening its lab doors with a program aimed at rising 10th grade girls. Stanford Artificial Intelligence Laboratory’s Outreach Summer program is designed to expose high school students in underrepresented populations to the field of Artificial Intelligence (AI). The two-week, full-time program will provide both broad exposure to AI topics through faculty lectures and industry field trips, as well as an in-depth experience with a research area through hands-on projects.

Deadline for applications for summer 2016 is March 15.

The Center for Data Science at New York University invites applications for positions as Moore-Sloan Data Science Fellows. These independent research positions are a prominent feature of the Moore-Sloan Data Science Environment at NYU, a multi-institutional effort funded in part by a generous grant from the Moore and Sloan Foundations. Profiles of the six current Data Science Fellows can be found at: http://cds.nyu.edu/people/.

Data Science Fellows will be expected to work at the boundaries between the data-science methods and domain sciences. They will lead independent, original research programs with impact in one or more scientific domains (natural science or social science) and in one or more methodological domains (computer science, statistics, and applied mathematics). They are also encouraged to develop collaborations with partners at the University of California, Berkeley, and the University of Washington.

Deadline to apply is Friday, April 1. Fellowship applicants should send a curriculum vitae, list of publications, and brief statement of research interests (no longer than 4 pages) to ds-jobs-group@nyu.edu, and also arrange to have three letters of recommendation sent.

How do you take your data analysis skills beyond Excel to the next level? By learning just enough Python to get stuff done. This hands-on guide shows non-programmers like you how to process information that’s initially too messy or difficult to access. You don’t need to know a thing about the Python programming language to get started.

Through various step-by-step exercises, you’ll learn how to acquire, clean, analyze, and present data efficiently. You’ll also discover how to automate your data process, schedule file- editing and clean-up tasks, process larger datasets, and create compelling stories with data you obtain.