Two pieces today.

Take this one with your morning coffee and cake– it’s Christmas Day, and whether you celebrate or not, you need something sweet because you deserve it.

Then, when you’re fully caffeinated and ready to face the New Year and the end of civilization as we know it, read “Learning to Die in the Anthropocene.” Sometimes, you need to look down into the abyss– I promise that you’ll still have to go to work tomorrow. So it goes.

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My newest writing piece: a profile on Pulitzer-winning data reporter, coffee-addict and Bostonian Matt Carroll!  His story is being told in Spotlight, a movie that has rave reviews and is in theatres now.

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There are people whom I admire and then there are people for whom every moment I spend with them, I want to soak up every possible molecule of being in their presence. Tai Jimenez is one of those people. Her dance class can only be described as a spiritual experience, because the focus is never on just the steps, but the intent and purpose of every step.

And that is what ballet should be all about. It should be a artistic and introspective life-long practice, just like yoga or meditation, because it is a beautiful and thoughtful practice. In reality, this is just about the polar opposite of how things tend to go down: even in the small beans non-professional no-name classes I go to, bitches be fightin’ for the barre, and everyone is feeling extra fat in their tights today. It’s a hierarchy of competition and insecurity that dissuades people and stifles creativity, even though the medium is so ripe for creation. Not always, of course, but often enough that it’s always a shadow chasing close behind the joy of movement.  On a larger scale, this feeling works in tandem with existing institutions that reward people of a certain build, skin color, class, and attitude, not always for the best results.

(This is often how I feel about the tech industry and academia as well.)

Anyway. Tai, who also writes, posts a great response on her blog to Peter Martin, the chairman of the School of American Ballet (SAB), when he asks her to join a new “diversity initiative” for the SAB. As the all-too-frequent token girl / token minority in many lab settings past, present, and future, to whom all the “women in tech”, “minority”, “gender”, and “diversity” inquiries are directed, I can relate to her frustration. Her response is gold:

“I have been giving the matter of whether or not to join the Diversity Committee some thought. With all due respect, if the School of American Ballet is serious about diversifying, they can start by hiring me as a ballet teacher. I am great.

This is not about me, and it is about me. Please tell Peter Martins that true diversity means the whole structure has to change. Is he ready for that?”

In a follow-up blogpost, she explains:

“My approach to teaching ballet is too different from theirs. They want to create dancers. I want to create an ecstatic moment of dancing. They teach one to master a certain style and technique. I teach dance as a tool for self-mastery. They teach people how to squeeze themselves into a certain look. I teach people how to love themselves as they are and to dance from there. They promote an ideal. I expose the myth. They teach competition. I foster community. They teach hierarchy. I restore sovereignty of self. They pick favorites. I acknowledge everyone’s medicine and stir it up good.

And sometimes, I play hip-hop. SAB ain’t ready for this jelly.”

Read her original posts here and here. And, if you’re in the Cambridge/Boston area, definitely, definitely take a class with Tai! She teaches at  Harvard (the classes are open registration) and I believe Jeanette Neil’s.

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Living in Holland

09 Jul 2015

Eight days before my twenty-third birthday, I embarked on what can only be described as a truly magical journey to three completely contrasting cities: Reykjavik, Amsterdam, and Prague. Along the way, I met a number of fascinating characters– some of them related to me. Here is an excerpt of my great aunt’s life, which she handed me a photocopy of on my way to the airport leaving Holland. 

* note: interactive version cross-posted on FOLD, a new publishing platform made by some friends @ the Media Lab.

Living in Holland

(a completely unbiased autobiography

written by my great-aunt Lenore

for the AWCA)

I am Cantonese but was born in Shanghai, then and now the most cosmopolitan city of China. Already at birth I was touched by the American brush. Both my parents had studied in the States, my dad receiving degrees from MIT and Case Institute and my mom from Columbia University.

There were seven of us, four girls and three boys. I was ‘sister three’… and we were naughty. One day, to give you an example, our gardener was taking a nap, snoring with his mouth open and we put a spoonful of salt on his tongue. My mom scolded us… severely I might add.

After the happy years trouble came, first, the Japanese occupation and then, finally, the communist takeover. We had to flee and since my dad was Minister of Transportation, he had the use of a government plane and we escaped by flying to Taiwan just in time. This all happened a few months before I was supposed to graduate from high school. Fortunately the government in Taiwan granted my diploma, which enabled me to go study in Michigan.

I enrolled at Michigan State University and turned into a party girl, within the limits of decency. Although I didn’t study too industriously, I succeeded in becoming a member of Pi Mu Epsilon (mathematics honorary), Iota Sigma Pi and Sigma Delta Epsilon (chemistry honoraries).

After obtaining my MS in Physical Chemistry I was ready for the world. But was the world ready for me? I decided they and I were going to find out, but in a somewhat warmer climate than Michigan. Go West young woman! I descended upon innocent Seattle (those were the days) and by my first day I had fond a small apartment and a job at the Analytical Department of the University of Washington. All I needed was a husband. So, I spent my weekends dancing and dating (Sleepless in Seattle). The Chinese boys didn’t want me. They want their women, demure, soft-spoken and obedient. A loud-mouth like me was out of the question. So, I had to turn to other prey. At the foreign students club, Cosmo, I cornered Jack Wiegman, a student at the School of Communications, and made him my husband, like it or not.

After he graduated, we moved to New York, where I found a job at my mom’s alma mater, Columbia University. After only one year, out of the blue, I was approached by American Cyanamid, Princeton, NJ, who invited me to an interview and then offered me a position as a research chemist. I succeeded to invent two chemical processes. My boss quickly put his name to one and the other one I was forced to sell for the generous sum of one dollar.

My husband was transferred to Amsterdam and so I was back at point zero. I decided to interrupt my career by having a son. However, when he was two and an intelligent big boy, I felt he was ready for kindergarten and, for me, it was time to resume my career by joining a division of the Dutch Akzo Chemical Corporation in Amsterdam. To put it mildly: I didn’t like it at all. Fortunately, after almost two years, opportunity struck by way of an ad for a position as a research chemist at the Dental Materials Science Department of the University of Amsterdam. Thanks to my work in the States I was chosen among 40 candidates to become Hoofdmedewerker which you could roughly translate as Associate Professor. This you could call the turning point of my life. I came up with two more inventions but— it’s an old story— the head of the department tried to usurp the credit. I fought all the way to the Board of the University and won, but, of course, I had to move. I joined the Electrochemistry Department where I started in a PhD program. In 1979, I obtained the degree with the thesis “The Kinetics of the Hydration of Calcium Sulfate Hemihydrate and Cement, investigated by an Electrical Resistance Method”. (Wow)

To complete the bragging: I am listed in the Marquis Who’s Who in Science and Engineering (US) and also in the International Biographical Centre (Cambridge, England). I also have been a long-time member of the ACS (American Chemical Society).

Six years ago I suffered a stroke. Since then I’ve been confined to a wheelchair.

Now, I have time to catch up on reading and to play with my grandchildren who live in the same building, a canal house on Keizersgracht. And then, of course, there is the AWCA of which I have been a member 29 years.






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Post-Barbeque Blues

05 May 2015

Post-Barbeque Blues

NOW the daffodil is beheaded

    and floating in her vase;

MAY has sunk into our bones.

    tortilla chips &

        beer &

        burnt sausages

fight with strawberry ice-cream

    on red Solo cups.

a restlessness halfway between

    play & EXHAUSTION:

        our skin is hot, but

        now the air is cold.

the little girl will go home

    & refuse her dinner

        just as surely as WE

        are HOPELESS to do anything but

            crawl into the sheets sun-drunk /

            on soccer-sweat;

listen to tapes of Jack Kerouac

    spin tales of desolation angels

                subterranean blues


                & wine.

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In my last post, I lamented a bit the potential for advanced technologies to reinforce existing biases at a quicker and larger scale, without either our awareness nor consent.

The issue of algorithmically detecting gender that I briefly mentioned is one that I’ve been struggling with this semester. Inferring gender can be crucial in revealing lags at publications and harmful disparities in behavior where diversity data is not provided, yet the act of classification can be morally and socially harmful.

Nathan Matias is a Ph.D student at the neighboring Civic Media Lab who has been a great ally in helping soften the steep learning curve of grad school and also in tackling gender/identity related projects. His master’s thesis work in Open Gender Tracker has led to many great posts on the Guardian’s Datablog.

Like a truly great friend, he not only answered my questions with regard to ethically detecting gender, he wrote a comprehensive blogpost about it. He highlights the practical realities of keeping work both computationally effective and ethically responsible. A must-read for anyone considering work in this area. Thank you Nathan!

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At the beginning of the seminar I’m taking in Race and Racism, we examined the historical origins of race– smirking at 19th century attempts to neatly classify and digest mankind into square boxes of color, things like Blumenbach‘s 5 categories (Caucasian, Mongolian, Ethiopian, American, Malay) and his offensive descriptions of the capabilities of each, how misguided they were, how clearly politically incorrect they are to us now.

One image stands out in particular– a chart, on the left axis, the races; on the right, a marking in each category of human competence, such that, summing up the checked boxes, one might literally rank different humans against another by racial category. The White man wins, in every grouping. It’s a glaring embodiment of early racist ideology, printed on paper, long since dismissed. It would be improbable to imagine a chart like that printed in a 21st century publication; the author would be fired, no doubt.

Yet the chart seemed eerily familiar to me. At first I couldn’t place it; I hadn’t seen it before. Then I realized: what it reminded me of was my own education, not in sociology or anthropology, but in Computer Science. As a graduate student, with a focus on Machine Learning and social applications, the very core of many of the algorithms I study and write is statistical classification, a major topic of research. What we are doing now– what Google does with its personalized search, what Facebook does with its unsettlingly accurate ads– is automating that same thought process of 19th century anthropologists. We look at a person, here, her technological imprint on the web, the traces she leaves, her purchase history, friends, and log-ins, then say: “what sort of human being are you”? And then, using our charts and tables– no longer printed ones, but weights on variables in our algorithms and databases of records– we first classify her, and after, yes, we rank her (sometimes we rank her first and then classify her, as well).

Users are commonly classified by gender, or what a machine can predict your gender to be, often quite accurately, and yes, different genders are ranked very differently. In the goods-driven world of online advertisement, a woman is worth differently than a man depending on what the product is, sometimes more, sometimes less. This entire pipeline is problematic to say the least; first, in the binary classification of gender; and then, in the ranking of different gendered individuals against one another; but our algorithms only reflect the constructs already driving these approaches.

Algorithms don’t quite yet classify users by race, or at least not outloud, because race is such a charged issue in most countries. But that doesn’t mean that when an algorithm doesn’t label a category outright, it doesn’t profile users. Algorithms, which learn, much like humans, based on history, only reinforce existing social constructs wherever they are used, because that is what they do: digest data, find a pattern, and make predictions according to that pattern. Harvard Professor Latanya Sweeney discovered that searches for racially-associated names were disproportionately causing targeted ads for criminal background checks and records to appear. These searches could go beyond offensive because targeted ads are reinforced by user behavior; if I click on that ad, I tell the machine that it was effective in targeting, reinforcing prejudiced thinking. Automated selection processes, which are beginning to gain popularity, such as automated admissions to schools and credit ratings, could cause real, harmful, physical ramifications.

This isn’t to say that there is anything inherently “evil” in Machine Learning itself; it’s a fascinating field of study, and could become a major tool in public health, disaster relief, and poverty alleviation. The machine is doing nothing new in its actions; it is merely a force multiplier of human behavior. I believe that classification is core to human cognition; and yes, we label others upon contact, always. Whether we like it or not, there is no way to escape being classified and classifying others. It’s impossible to meet someone without assigning some kind of underlying worth to them; it sounds ugly out loud, but our classification are essentially value-laden in order to be useful.

Ultimately, our machines only reflect our selves: it is vital to realize that computers are human, raised on human values, and there is no such thing as objective computation. The question that remains is: what kind of value systems will we feed our algorithms?

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Go here now, read and weep. To people who think that sexism in STEM is just hype, let me tell you that unfortunately, I have *NEVER* worked in a single lab or workplace of a technical nature where I have not experienced some sort of sexism– ranging from inappropriate comments to outright harassment. That is right– not a SINGLE lab or company. I would say my resume contains places people would consider inspiring, cutting edge, and liberal. Please be respectful and think about that.

Hattip @Mathbabe !

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twitter X MIT

01 Oct 2014

we’re launched!

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There is something about Trains that I Like. Out of planes and trains and automobiles they to me are the most romantic. Nine-eleven sort of killed flying and even before that I always hated being in the air. What a cruel trick this was, I thought, as a child, to call it flying but being so utterly trapped in my seat, the opposite of free.

Cars were nice enough; I just didn’t like driving them. It was as if all the worst stereotypes joined forces (Asian; woman; short-tempered; small) and at 5’1” I could barely look over the steering wheel clearly. Once I had a conversation in the basement of a boy I convinced myself I loved in high school, about what the spirit of a car looked like. The beer was brown and in a glass (this was in the end of college when I first learned that beer was meant for a cold tall glass) and he said the spirit of a car in its highest form was a thing of beauty, because really, this was the spirit of a free man. And when you drove that car, really, you were just taking that thing of beauty where it wanted to most go. I thought that was fine as long as a thing of beauty was driving the car itself and I was free to look at both.

But trains. Trains were lovely because you were always moving but you never had to worry about how. There was something blue collar about them and it was great; you ran down the stairs in Penn Station onto the platform with all the hordes of tired businessmen with their beer in brown paper bags the smell of artificial butter popcorn in the air; shooting the shit with the conductors, sometimes jolly more often tired. And best of all you could look out the window and see the grass roll by; it was best especially in New England when the leaves changed and when you rolled past Connecticut dreaming of what doctors and dolled-up wives were behind those white picket fences. Then you thought it would be nice to be invited somewhere fancy for a change because then you could wear sparkly things like the best of them and be admired, but you were raised too pridefully to ever desire such things. To be rich. To be a decorative. It was folly.

And you were always going somewhere. I am talking only about American trains, I can’t speak for any other kinds. It was the American train I loved with those dreams of railroads and going to New York with just a backpack, a handle of bourbon in it and a note from your sweetheart. Maybe going up North through Appalachians and all you had was some E.E. Cummings with you and some pen and paper to write the next big thing. You with all the drunks and suits and so long as it wasn’t St. Patrick’s day on the New Jersey transit it was all beautifully coarse. It was so Americana; and this to a little Chinese American girl was somehow the best thing of all, even if the Amtrak was making nothing but debt and really the tracks weren’t very safe, and no one could afford a damn ticket anyway.

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