On magic

05 Apr 2018

I think about magic often, and what that word means to me.

I think that magic means that something occurs, and we do not need (or really want to find) evidence of how it occurs. Magic means that the emotional truth of the occurrence is enough.

Cambridge is a place where atheism can feel like the predominant religion. There are not many people who are outwardly religious, although they might be inside.

But every time I had an appointment to see the ear surgeon, a man would sit across from me on the bus, urgently crossing himself, clutching rosary beads. He was not an old man and not a very young one either. Maybe in his thirties, slightly overweight, wearing brown leather shoes, a black peacoat, and on some days, a blue collared shirt, which is how I noticed that his eyes were blue.

In other words, he looked perfectly ordinary. The only reason he stood out because his prayers were so fervently strong, and everyone else on the bus was so mindlessly in transit.

Every bus ride I took, he would take too, even though the appointments weren’t all at the same time. He would sit, mumbling to himself, crossing himself, praying as the sun streaked through the bus and people bumped and jostled around him.

I live next to a big, beautiful cemetery. The first time I wondered if he was a priest and he was going to a funeral to lead a service. I grew up with so little concept of religion, even as an adult I’m not sure if all the words I just said are the right ones for that sentence. But he never got off at the cemetery.

The third time I shared a bus with him, I decided that it must have been more than coincidence. I eyed the man on the bus, and followed him ten steps behind as he took the subway, in the same direction as me, from Harvard. I sat with him in the same train car, but not in the same row, so that I could peek at him in my periphery.

I expected him to get off at MGH with me, because where else could one go in such feverish worry if not a funeral or a hospital?

But he didn’t.

After I got better and no longer needed to go to the hospital every week, I often wondered who he was praying for. But I never asked him or even spoke a single word to him, although we made eye contact once. The eye contact was neither warm nor suspicious, it was simply weary.

I thought that he might have been a magical creature and when you see a magical creature at work you should not stop to question their magic. I thought he might have been praying for me.

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Rainy Day Poem (2016)

08 Jan 2018

Rainy days in suburbia are dull and depressing,

but in New York City they are calm and beautiful.

The City itself shines so bright that on sunny days,

the compounding effect verges on manic.


Rain, not just April showers but the





gets into the best-made plans and

best-shopped shoes of New Yorkers

forcing pedestrians and subway-goers alike to




curse at the sky,

and come to terms with their own soggy mortality.

There is a unified serenity in the defeat of perfection.


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Originally posted on Medium.

This is Mayfair Street in Northeast Philly.

It looks like any other street in the city, with little row houses packed together like sardines. Not unlike the Philadelphia row house I spent the first year of my life in after my parents immigrated from China to the States.

But if you squint at the vines crawling on the wire fences, you’ll see that there are bitter melons hanging off the vines, not just purple morning glories.

Yesterday morning I woke up way too early and hesitantly joined my sister and her friends to Get Out the Vote in Pennsylvania, a “battleground” state. Anyone who knows me knows that I hate approaching strangers, least of all to ask them to do something.

The day started off slow, with the sun beating down on a unusually warm November day. Most people were at work, and once a little boy peeped through the blinds — only to bolt the door shut.

Halfway down the street, I knocked on a door and an older Asian gentleman answered.

When I began my spiel, he gestured with his hands. Expected to be waved away again, I began to back off.

But when I leaned closer, I realized what he was saying was no to English, not no to me. Unfortunately, he began speaking in Cantonese, a language I don’t know a word of. As a last ditch effort, I tried to ask him if he’d voted in my third-grade proficiency Mandarin.

He replied!

Mr. Lee* (whose name I learned at the polls) told me people had been knocking and calling all week, but not a single person spoke a word he could understand. He tried to vote this morning, but it was too confusing.

He went home.

Did I know who he wanted to vote for, I asked? He did.

“The Woman,” he said.

Reluctant to abandon my check sheet of addresses and names, I called the voting hotline. Did they have translators at the polling station nearby? No one who spoke Chinese, no. I could, however, help interpret for a voter, and accompany them to the polls.

Ten minutes later, we walk together to Creighton Elementary School. Along the way, he tells me a little bit about his life. It’s isolating to not speak English here, but he’s too old to learn. Mr. Lee casts his first ballot, and we both get an “I Voted” sticker, me for the ride.

On our walk back to Mayfair Street, I ask him if any of his friends need help too. He calls some friends he can think of, but most of them are at work at this time of day.

A young woman in a Cal t-shirt runs past and he shouts her name and they start chatting faster than I can understand for a few minutes. Excitedly, she turns to me, and then runs down the street. “Let me just grab my ID!” she says in Mandarin.

Together, I go with her again to the polls — but unlike with Mr. Lee, there’s no time for small talk. Hurry, we have to walk fast, she says. She works 8am to 8pm so there’s no time for her to vote, but she managed to get someone to cover her shift just now. I add another sticker to my shirt, to the smile of the polling volunteer.

                                   Picture from Equality PA

In more than three hours of walking and knocking and trying to come up with a universal language with the people I meet, I’m able to help three voters make the long journey just a few blocks away.

For a scientist who is used to observing human behavior through “big” data, three seems like a defeatingly small number. But in terms of human lives, it is infinite.

Yesterday I was reminded that despite all the observations we can do from 3,000 feet, it’s important to get on our feet. Not only to be reminded of what’s at the heart of it all, but because many issues are impossible to comprehend without knocking on the doors of strangers.

By meeting potential voters like Mr. Lee, I realized that there was a real shortage of interpreters in Philly, especially for Asian Americans. Later, I learned that this has been a contested issue in many districts since the 2014 midterm elections.

Although Election Day was only a single date in the calendar, I have spent the past two years analyzing, speculating, and writing about the elections during my Master’s degree. Yet I rarely discussed politics outside of my circle of friends and colleagues.

If data science is about the art of drawing patterns from numbers, there is a stronger and more difficult art in drawing the human stories out of data. Although the introvert in me wishes to disagree, there is no way to write a story without looking at another human being in the eye.

In times of disappointment, when it seems so hard to move the needle forward, find solace and meaning in the words of Nobel laureate Toni Morrison. She reminds me of my purpose:

“To pursue the human project — which is to remain human and to block the dehumanization of others.”

* Name has been changed for privacy.

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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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