MY BOOK CHAPTER! The architecture of Fermilab

I’m a science writer by profession (obviously), but occasionally I get the chance to write about something fun that’s only tangentially related to science. A while back, Belt Publishers — which publishes books and a magazine about the part of the American Midwest known as the Rust Belt — solicited pitches for chapters on a book about Midwestern architecture, and I sent them (shhh) a portion of my book I couldn’t get published. Belt liked what I sent them, and the result is I have a chapter in the forthcoming anthology Midwestern Architectural Journeys (edited by Zach Mortice), available October 15, 2019!

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When Brilliant Physicists Toiled Under a Beer-Can Roof

The inspired and eccentric design of a hub of Cold War physics research, the Fermi National Accelerator Lab in Illinois

One thing I didn’t have space to write about: one of the physicists who led an experiment at Fermilab was neighbor to New Yorker cartoonist George Booth. Their friendship led to Booth designing a mascot for the experiment, which ultimately wasn’t used, but still graces the outside of one of the buildings. [Credit: moi]

Chapter excerpt published by CityLab:

I didn’t come to the prosaically named Silicon Detector building for its roof. I was there to look at some cutting-edge telescope technology, soon to be implemented at one of the world’s leading observatories. But here I was looking up at the interior of a funky squashed geodesic dome, constructed of triangles in muted reds, blues, and golds, like an electron micrograph of a virus built of stained glass by Buckminster Fuller.

The Silicon Detector (or SiDet) building itself is a squat concrete structure with sloping sides and a trapezoidal profile, a distinctly 1970s structure. The geometric dome originally was intended to be a patriotic red, white, and blue, but time has faded it into autumnal colors. The panels are made out of recycled beer and soda cans with their ends cut off, arranged between two sheets of colored plastic reinforced with glass. Light shines through the cans, but not so brightly as to create a glare.

The SiDet building is all the more striking for what and where it is: It’s a physics lab devoted to the fabrication of next-generation detectors for experiments and telescopes. More specifically, SiDet was originally part of a facility meant to study neutrinos: very fast-moving, low-mass particles that are notoriously hard to detect. Similarly, the facility itself is hidden from the general public’s view behind a security perimeter on the grounds of the Fermi National Accelerator Laboratory, more commonly known as Fermilab.

[Read the rest at Citylab, and order the book from Belt]


Protecting privacy with mathematics

The linked article is for SIAM News, the magazine for members of the Society for Industrial and Applied Mathematics (SIAM). The audience for this magazine, in other words, is professional mathematicians and related researchers working in a wide variety of fields. While the article contains equations, I wrote it to be understandable even if you skip over the math.

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Using Differential Privacy to Protect the United States Census

Census data must simultaneously be publicly available and protect the privacy of the people it describes. Differential privacy is a method that injects noise into the data to hide the presence of individual responses, while preserving the general statistical structure of the data. [Credit: moi, which is why I’m not a professional graphic artist]

For SIAM News:

In 2006, Netflix hosted a competition to improve its algorithm for providing movie recommendations to customers based on their past choices. The DVD rental and video streaming service shared anonymized rental records from real subscribers, assuming that their efforts to remove identifying information sufficiently protected user identities. This assumption was wrong; external researchers quickly proved that they could pinpoint personal details by correlating other public data with the Netflix database, potentially exposing private information.

This fatal flaw in the Netflix Prize challenge highlights multiple issues concerning privacy in the information age, including the simultaneous need to perform statistical analyses while protecting the identities of people in the dataset. Merely hiding personal data is not enough, so many statisticians are turning to differential privacy. This method allows researchers to extract useful aggregate information from data while preserving the privacy of individuals within the sample.

“Even though researchers are just trying to learn facts about the world, their analyses might incidentally reveal sensitive information about particular people in their datasets,” Aaron Roth, a statistician at the University of Pennsylvania, said. “Differential privacy is a mathematical constraint you impose on an algorithm for performing
data analysis that provides a formal guarantee of privacy.”

[read the rest at SIAM News…]