NYU Data Science newsletter – August 18, 2015

GROUP CURATION: N/A

For the last 5 years I’ve been actively involved in training efforts through Software Carpentry and Data Carpentry to train researchers in best practices for software development and data analysis. These are concepts that are fundamental to the research we do in my gropu and my commitment to open and reproducible research.

As one of the founding members of the Data Carpentry Steering Committee, I am excited to announce that Data Carpentry has received a grant from the Gordon and Betty Moore Foundation that will help support our work over the next two years.

At Zipongo, our mission is to make healthy eating easier and more convenient. Food is one of the top drivers of chronic disease risk, so we focus on personalized and realistic ways to improve our users’ food choices.

Everyone needs to eat, but we all have different health concerns, lifestyles, habits and food preferences. These differences are shown in our users’ behaviors. For example, our male users are 20% more likely to choose recipes with the word “creamy” in them than our female users.

We also know that various diets or ways of eating work better for different people depending on health conditions, genetics and behavioral habits. This means we can’t give generic nutrition advice like “eat more kale.” In order to have an impact at the population level, we have to provide personalized and actionable recommendations for individuals.

The first thousand-robot flash mob has assembled at Harvard University.

“Form a sea star shape,” directs a computer scientist, sending the command to 1,024 little bots simultaneously via an infrared light. The robots begin to blink at one another and then gradually arrange themselves into a five-pointed star. “Now form the letter K.”

The ‘K’ stands for Kilobots, the name given to these extremely simple robots, each just a few centimeters across, standing on three pin-like legs. Instead of one highly complex robot, a “kilo’ of robots collaborate, providing a simple platform for the enactment of complex behaviors.

… University of Washington machine learning researchers have developed a radically new approach to optimization, in part by borrowing a classic technique from artificial intelligence and computer science. The paper outlining their approach won the top prize in July at the 24th International Joint Conference on Artificial Intelligence, the world’s largest AI conference.

In two applications that were tested experimentally, the new UW approach outperformed standard optimization techniques, in some instances by many orders of magnitude.

Social media, content, and commerce sites continually experiment on their users to improve products and services. Yet occasionally these experiments backfire as seen in the Facebook emotional contagion study (2014) or Amazon’s alleged price discrimination experiments. This panel brings together experts from academia and industry to reflect on the lessons learned from both quiet successes and dramatic failures to offer you practical advice on how to design and conduct online experiments to improve your sites without upsetting your customers or gaining unwanted negative publicity.

Deadliine for voting to see this panel included in SXSW 2016 is Friday, September 4.

Do you love animating data, creating science apps, or taking macrophotographs? In the Visualization Challenge, sponsored by the National Science Foundation and Popular Science, your handiwork can receive its due glory and win you cash prizes. Contest winners will be announced in February 2016, and will be featured in the March issue of Popular Science, on popsci.com and nsf.gov/news/vizzies.

Deadline for entries is Tuesday September 15.