Egg: a place for science stories to nest


Frustrated with the perception of certain sciences as “cold” and the programmer-storyteller divide, I wanted to create a space with tools and stories that gently remind us there is in fact no such divide aside from the ones in our beliefs.

Here’s a prototype, without uploading capability.

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You Are Not Alone: Sexual Assault at MIT

In response to this New York Times article which is in turn in response to this climate survey by MIT, I’ve made an infographic on how one might go about speaking up and reporting their own experiences– the first step to tackling a deeply traumatizing and complicated issue. One of the most terrible aspects of being sexually harassed is the feeling of both being utterly alone and having to retell one’s story over and over again. Facts and figures from this survey proves that one is, unfortunately, rarely alone and that there are resources to report anonymously to– hopefully fueling greater cultural and civic change to our treatment of these problems.


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Boston’s Urban Orchards

With the weather becoming warmer again, for this week’s assignment I reported on a lighter and sweeter topic: urban orchard’s in the Boston Area.

I was fascinated to learn that there are, in fact, fruit trees and berry bushes around Cambridge, Somerville, Boston, and more that are publicly foragable. This dataset from the city of Boston data portal lists the known plants, and I overlayed it onto a color coded map.

I was unable to get the embed working in wordpress which does not allow for iframes, so here’s the link!

(How) Can Algorithms be Racist?

Technology can be the ultimate equalizer: once access is provided, it can erase borders, education, race, class. But a new study offers that the same tools that are said to provide a level playing field might also be blind spots.  Are the algorithms that are used to drive images and ads perpetuating human prejudices?  One study says yes. But, how can algorithms (which seem to be based on reason) discriminate?

Flash preview: (How) can algorithms be racist? An illustrated story #doodles #datamining #race #partnews

A video posted by Sophie C (@petit.chou) on

For this assignment, Alicia and I wanted to tackle the issue of bias and discrimination in algorithms in a creative way. Our response is to this short article from the Guardian, “Can Googling be Racist?“.  The Instagram video is a preview of the resulting story, which I plan to scan into a static web-readable series.

To explain, we  supplemented Latanya Sweeney’s research paper with my own knowledge of data mining and algorithms, in a easily-digestable format. One of my biggest gripes as a computer scientist/machine-learner is the assumption that algorithms are either value-free or a mysterious black box. As Mark Twain (might have) said,

“There are three kinds of lies: lies, damned lies, and statistics.”


Adventures in Obtaining Diversity Metrics

This week’s assignment was about debunking myths: employing methods to shine truths into the minds of readers who are often systematically, politically, stubbornly biased against the facts given.

Often it is mind-boggling that these myths are able to persist: issues like human-caused climate change are so heavily supported by scientific evidence and harbor such great consequences, it seems that common sense calls for a belief in its existence. Mythbunking, however, is rarely about common sense, and almost always about human psychology. It’s a surgical operation: “when you debunk a myth, you create a gap in the person’s mind. To be effective, your debunking must fill that gap” [the Skeptical Science Debunking Handbook].

A myth, and thus mythbunking, therefore, seems to be defined not by what class of problem the issue is in (environment, vaccination, gun control), but rather one’s attitude towards the concept at hand. When there is a lag between perception and reality, and an avoidance to voice the reality, despite the support of statistics, then we have a myth of the Skeptical Science kind. Even though the issue that I ended up exploring in the end isn’t as clearly divided as black and white (or maybe it is precisely too much black and white), it follows all of these trademark characteristics.

Although it would be hard to believe that anyone who has spent time at MIT to refuse to acknowledge any skew in demographics, the narrative that emerged out of my attempts to retrieve facts about the degree in which diversity was a problem in recruiting and admissions reinforced myths more subtle and potentially harmful– those of our own misguided perceptions.

image (2)

Gender Representation is significant issue in both student and faculty body.

In terms of sheer numbers, there are far fewer women than men throughout the lab. In a total of 198 students listed on the Media Lab webpage, only about 25% (or 50 students) were female. 7 out of 27 groups, or about 26% (my own included) have only 1 female student.

Tokenization (not as in lexical analysis, but as in there is a token representation of a minority) is harmful in that it encourages stereotype threat: the risk of confirming to stereotype, leading to underperformance of the individual in a workplace or academic environment (for a summary, see this fact sheet).

Faculty fares even worse in terms of gender, with only 5 female professors out of 31 total, or 16%. Of those 5 female professors, only 2 are tenured faculty, or 6%. This is half the national average for Engineering, last measured in 2011 by the NSF.

Moreover, in my attempts to obtain these statistics, I found it difficult to get a unified response from directors and administrators. In the end, I was advocated against using the data that I had previously found from the official Diversity Committee, whose website does not contain any concrete figures.

As a result, I used language processing on the listings on the Media Lab website and inferred the most probable gender of students and faculty by name, using the Gendre API which searches databases of first and last name combinations by country. It is important to note that my counts are of inferred gender by probability, and an estimate of how a student might actually self-identify.

Although the female students and faculty who are within the lab have a strong and significant presence (such as Pattie Maes, who is the Associate Head of the MAS Program and whose brief report earlier this month prompted me to explore this issue in the first place), this lends an exaggerated effect of parity, which isn’t the case.

The Media Lab has an even bigger problem with minority representation. Whereas gender inequality has made some small but not insignificant strides in the last two decades, the number of URMs (under represented minorities) has stayed extremely low and in some cases worsened. I was asked not to cite these materials in my work. I was not able to use linguistic analysis to try to infer people’s race and ethnicity, for both practical and ethical reasons.

Hacker, Maker, Chinaman: a story about the Maker Movement in US and China as told by Celeste LeCompte

On a sunny Monday in Cambridge, I sat down with Nieman fellow Celeste LeCompte to interview her. Based on my last-minute Googling, I’d discovered that she had an interest in robotics, the environment, and China. Hoping to get the scoop about Environmental issues in China or the experience of being a foreigner traveling in the country, I came prepared to learn some interesting details about that. Instead, a fascinating story about the parallels between the Maker movement in the China and US, along with the discussion of what exactly it means to be a “Maker”, emerged. Thank you, Celeste!

Hacker, Maker, Chinaman

Stale off the Boat with Eddie Huang

Notes: For this four-hour assignment, I watched episodes 3 and 4 of the new sitcom, Fresh Off the Boat, with 3 other Asian-American friends and afterwards debated whether or not the show was realistic, racist, or any good at all. Then, I went home and wrote a review.

Stale off the Boat with Eddie Huang

By episode four of the “groundbreaking” Asian-American sitcom, I’m starting to see what Eddie’s angry about.

image from Hollywood Reporter

Fresh Off the Boat, a new ABC sitcom based off the adolescence of Eddie Huang, big restaurateur (he owns the popular Baohaus in the East Village) and even bigger personality (he’s a regular on VICE and prone to dropping four-letter-words along with extended Frankenstein ones of his own creation left and right in every interview), drew some controversy for using the racial slur “chink” in the pilot episode. The scene goes like this: in white-washed Orlando, Florida, where young Eddie is an outcast newcomer, the only other kid of color, a black boy, pushes him out of the way in the lunch line and onto the bottom of the middle school totem pole– a place he used to inhabit de-facto.

In isolation, it’s a simple act of pre-teen territorial marking, some standard name calling pushing the biggest button an 11-year old can think of, the race card. But here’s the thing. It’s more complicated than that– what little Eddie, whose idols are all black rappers with big swagger, living the FBGM life– wants most is to be accepted by his white schoolmates. To be a Lunchable, pizza flavor. Walter, the offending name-caller, says it best, after Eddie chooses the shaggy haired popular crowd over him, with a roll of his eyes: “What kind of country is this, where a white kid and an asian kid bond over a black guy?”.

I find myself asking the same thing about the show. Despite all the racially colored, exaggerated antics of the Huangs, there is very little substance addressing the so many obvious racial questions we’re left wanting to ask. Why is the show called Fresh Off the Boat, a racialized slur that in my experiences is far more common and loaded than the above offense, when Eddie’s family is fresh off the boat at all, but fresh off a car drive from another major U.S. city? Did little Eddie have black friends in DC, where his family recently moved from and which is significantly less white-washed than Orlando?

Hip hop is so clearly an inspiration to Huang, but we only ever see it repurposed in the hands of white or asian kids. What young Eddie aspires to the most is the image he’s formed in his head of black masculinity. He wants honeys (literal Honeys, in Episode Four, where he tries to win over his sexy new (married adult) neighbor– was that a nod to Biggie’s lyrics?) playing him close and a soundtrack to go with his swagger (albeit currently played on a boombox by his grandmother).

It makes great sitcom fodder, because to the viewer, there’s nothing further away from a black rapper than the fat little asian kid, eating tofu and being told to do his math exercises by his Dragon-lady mother and his contentedly obedient younger siblings. Hilarity and entertainment ensues, but doesn’t the fact that we the viewer find it comedic at all, affirm, on some level, that the struggle is real?

For Huang, the real life one, the struggle is exactly that: what he calls “breaking the bamboo ceiling”, or the stereotype of model minority. The show, although at times endearing and “aww”-inspiring in an overstated way (parents making up after a fight, cute child actors being cute) fails to do that. At most, it puts a more human face to a heavily-stereotyped, fantasy Asian-American family. Which isn’t to call it a trivial feat: after all, this is the first Asian family on TV ever.

Eddie, as expected, had harsher words: “The network tried to turn my memoir into a cornstarch sitcom and me into a mascot for America. I hated that”[1]. As for me, I’m left wondering what all this means to the little Asian kids out who grew up listening to Biggie and Nas and Tupac (if we must include the West Coast) to fuel their swagger. What about girls, who have two ceilings to break: bamboo and glass (that’s another thing; aside from the mother figure, this show is a Boy’s world). Are we all just material for laugh tracks?

Still, any depiction of Asian-Americans that brings at least more than one-dimension to the unexplored arena can be a welcome one. Despite all the unanswered questions and the heavy-handed reliance on tropes, it’s a step in, if not the right direction, at least some sort of movement. As for the opinion of this little “Chinkstronaut”? I go home, pull out my laptop, and blast some Notorious B.I.G.

In the words of Biggie Smalls: It’s all good, baby baby.


Sophie’s Media Diet

Sophie's Media Diet


Media– like food– has become an irreplaceable part of our daily diet. To many people, not being able to scroll through the news, to check Gmail, to check Twitter, to check Facebook for a day or two feels like a real fast. Our hunger for media often parallels our cravings for calories– the later in the night it is, the more we want salty, addictive snacks for mindless consumption.

With this in mind, I spent one week (2/4-2/11) recording what sort of media I was consuming every time I ate. The most repeated results are what I’ve used to put together the infographic above. Because I absolutely adore food and cooking, I actually spend a fair amount of my day not only digesting media and food but also media on food. Food blogs, like Serious Eats and the Kitchn, are my favorite, and I read them daily like newspapers.

The results are telling– I do, in fact, tend to eat “slower” foods with longer forms of media– such as dinner with Netflix, and a Cafe au Lait with the New York Times. Recently, I’ve been working on a project that involves scraping and parsing several media outlets — thus consuming tons of articles and stories without actually reading them. When I code, I tend to eat nothing at all, or salty, repetitive foods such as goldfish to match the rate in which I’m doing things.

It should be noted, that of course, what is shown is an incomplete picture– I tend to eat a lot more of both food and media than shown; what is chosen is curated for the overall summary.