Posted by Rob Knies

On Feb. 28, at the Santa Clara (Calif.) Convention Center, Kate Crawford, principal researcher at Microsoft Research New England, took the stage during the Strata Conference to deliver an illuminating, 17-minute talk entitled Algorithmic Illusions: Hidden Biases of Big Data.

During that presentation, she cautioned that data and collections of data are not objective. They are created and shaped by human beings, and understanding the unavoidable hidden biases people bring to data collection and analysis can be as significant as the data themselves.

Now, on the heels of that appearance, Crawford is bringing a similar message to a different audience, that of the Harvard Business Review, which has just published her contributed article, Big Data Has a Signal Problem, that underscores the concepts she discussed during Strata 2013.

Posted by Kelly Berschauer

The Association for Computing Machinery (ACM) has a history of conducting successful student competitions during its major conferences, so it was only fitting that when Microsoft Research Connections and Microsoft Research Silicon Valley were considering hosting a similar event in the latter’s Mountain View, Calif., facility focused on research, they should turn to the ACM model.

The student research competition, hosted in conjunction with the Simons Institute for the Theory of Computing, based at the University of California, Berkeley, was held March 25 with the goal of connecting local students with local research organizations around the globe. Arjmand Samuel, senior research program manager for Microsoft Research Connections, indicated that he hopes the event serves as a precursor to a trend.

Posted by Rob Knies

What makes you happy? Mary Czerwinski is performing some research on that very topic.

Czerwinski, a principal researcher at Microsoft Research Redmond and manager of the Visualization and Interaction for Business and Entertainment Research Group, is participating on the committee for The H(app)athon Project, a big-data effort to use happiness indicators to harness the power of emerging technologies to help the world measure happiness.

Such indicators already are being used in Bhutan and the United Kingdom to measure the well-being of citizens. The project aims to connect the tools people use to measure themselves with metrics used to measure humanity, beginning March 20 in New York, London, and Tokyo with awareness events featuring presentations about the synergies between well-being, emerging technology, and happiness indicators.

Posted by Rob Knies

It’s no surprise, really, that danah boyd, senior researcher at Microsoft Research New England, has been named the second inductee into the South by Southwest (SXSW) Interactive Festival Hall of Fame.

After all, boyd has established herself as one of the world’s leading lights when it comes to analysis of trends at the intersection of technology and society—especially when it comes to youth culture.

The honor, presented March 12 during the SXSW Interactive Awards, is intended to recognize trendsetters whose career accomplishment s have paved the future of the new media industry.

Posted by Rob Knies

You might have heard of Drew Purves before. As head of the Computational Ecology and Environmental Science Group at Microsoft Research Cambridge, he has gained attention for a number of his projects, from modeling forest dynamics to FetchClimate, that use big data and build models to make predictions about future environmental conditions.

He’s back at TechFest this year with a different kind of project. Called Predictive Decision-Making at the Speed of Thought, this effort provides the fundamental research needed to build predictive models for many different types of data, not just the environmental. The goal is to generalize the approach he has been pursuing to make such tools available to a broad range of organizations and businesses.

“In the environmental sciences where we work, but also much more broadly, there’s a lot of demand for the pipeline that goes from big data through models to predictions of important things,” Purves explains. “We know, fundamentally, how to do that, but the technical barriers at the moment are so high that it’s the domain of specialist experts, which, in turn, means that it’s only the world’s largest organizations that can afford to support that kind of data-to-prediction pipeline.”