Emily Shur Chris McKinlay was folded into a cramped fifth-floor cubicle in UCLA's math sciences building, lit by a single bulb and the glow from his monitor.
The subject: While the computer chugged, he clicked open a second window to check his OkCupid inbox. McKinlay, a lanky year-old with tousled hair, was one of about 40 million Americans looking for romance through websites like Match. He'd sent dozens of cutesy introductory messages to women touted as potential matches by OkCupid's algorithms.
Most were ignored; he'd gone on a total of six first dates. On that early morning in June , his compiler crunching out machine code in one window, his forlorn dating profile sitting idle in the other, it dawned on him that he was doing it wrong. He'd been approaching online matchmaking like any other user. Instead, he realized, he should be dating like a mathematician. OkCupid was founded by Harvard math majors in , and it first caught daters' attention because of its computational approach to matchmaking.
Members answer droves of multiple-choice survey questions on everything from politics, religion, and family to love, sex, and smartphones.
The closer to percent—mathematical soul mate—the better. But mathematically, McKinlay's compatibility with women in Los Angeles was abysmal. OkCupid's algorithms use only the questions that both potential matches decide to answer, and the match questions McKinlay had chosen—more or less at random—had proven unpopular. When he scrolled through his matches, fewer than women would appear above the 90 percent compatibility mark.
And that was in a city containing some 2 million women approximately 80, of them on OkCupid. On a site where compatibility equals visibility, he was practically a ghost. He realized he'd have to boost that number. If, through statistical sampling, McKinlay could ascertain which questions mattered to the kind of women he liked, he could construct a new profile that honestly answered those questions and ignored the rest.
He could match every woman in LA who might be right for him, and none that weren't. He then sorted female daters into seven clusters, like "Diverse" and "Mindful," each with distinct characteristics. Maurico Alejo Even for a mathematician, McKinlay is unusual. Raised in a Boston suburb, he graduated from Middlebury College in with a degree in Chinese. In August of that year he took a part-time job in New York translating Chinese into English for a company on the 91st floor of the north tower of the World Trade Center.
The towers fell five weeks later. McKinlay wasn't due at the office until 2 o'clock that day. He was asleep when the first plane hit the north tower at 8: The experience kindled his interest in applied math, ultimately inspiring him to earn a master's and then a PhD in the field.
First he'd need data. While his dissertation work continued to run on the side, he set up 12 fake OkCupid accounts and wrote a Python script to manage them. The script would search his target demographic heterosexual and bisexual women between the ages of 25 and 45 , visit their pages, and scrape their profiles for every scrap of available information: To find the survey answers, he had to do a bit of extra sleuthing.
OkCupid lets users see the responses of others, but only to questions they've answered themselves. McKinlay watched with satisfaction as his bots purred along.
Then, after about a thousand profiles were collected, he hit his first roadblock. OkCupid has a system in place to prevent exactly this kind of data harvesting: It can spot rapid-fire use easily. One by one, his bots started getting banned. He would have to train them to act human. He turned to his friend Sam Torrisi, a neuroscientist who'd recently taught McKinlay music theory in exchange for advanced math lessons. Torrisi was also on OkCupid, and he agreed to install spyware on his computer to monitor his use of the site.
With the data in hand, McKinlay programmed his bots to simulate Torrisi's click-rates and typing speed. He brought in a second computer from home and plugged it into the math department's broadband line so it could run uninterrupted 24 hours a day.
After three weeks he'd harvested 6 million questions and answers from 20, women all over the country. McKinlay's dissertation was relegated to a side project as he dove into the data. He was already sleeping in his cubicle most nights.
Now he gave up his apartment entirely and moved into the dingy beige cell, laying a thin mattress across his desk when it was time to sleep.
For McKinlay's plan to work, he'd have to find a pattern in the survey data—a way to roughly group the women according to their similarities.
The breakthrough came when he coded up a modified Bell Labs algorithm called K-Modes. First used in to analyze diseased soybean crops, it takes categorical data and clumps it like the colored wax swimming in a Lava Lamp. With some fine-tuning he could adjust the viscosity of the results, thinning it into a slick or coagulating it into a single, solid glob.
He played with the dial and found a natural resting point where the 20, women clumped into seven statistically distinct clusters based on their questions and answers. Another pass through K-Modes confirmed that they clustered in a similar way. His statistical sampling had worked. Now he just had to decide which cluster best suited him. He checked out some profiles from each. One cluster was too young, two were too old, another was too Christian.
But he lingered over a cluster dominated by women in their mid-twenties who looked like indie types, musicians and artists. This was the golden cluster. The haystack in which he'd find his needle. Somewhere within, he'd find true love. Actually, a neighboring cluster looked pretty cool too—slightly older women who held professional creative jobs, like editors and designers.
He decided to go for both. He'd set up two profiles and optimize one for the A group and one for the B group. He text-mined the two clusters to learn what interested them; teaching turned out to be a popular topic, so he wrote a bio that emphasized his work as a math professor.
The important part, though, would be the survey. He picked out the questions that were most popular with both clusters. He'd already decided he would fill out his answers honestly—he didn't want to build his future relationship on a foundation of computer-generated lies. But he'd let his computer figure out how much importance to assign each question, using a machine-learning algorithm called adaptive boosting to derive the best weightings.
Sex or love? Love, obviously. But for the younger A cluster, he followed his computer's direction and rated the question "very important. At the top: He scrolled down Ten thousand women scrolled by, from all over Los Angeles, and he was still in the 90s. He needed one more step to get noticed. Women reciprocated by visiting his profiles, some a day.
And messages began to roll in. Thought I'd say hi. Only one thing remained. He'd have to leave his cubicle and take his research into the field. He'd have to go on dates. Sheila was a web designer from the A cluster of young artist types. They met for lunch at a cafe in Echo Park. He went on his second date the next day—an attractive blog editor from the B cluster.
He'd planned a romantic walk around Echo Park Lake but found it was being dredged. She'd been reading Proust and feeling down about her life. Date three was also from the B group. He met Alison at a bar in Koreatown. She was a screenwriting student with a tattoo of a Fibonacci spiral on her shoulder.
McKinlay got drunk on Korean beer and woke up in his cubicle the next day with a painful hangover. He sent Alison a follow- up message on OkCupid, but she didn't write back. The rejection stung, but he was still getting 20 messages a day. Dating with his computer-endowed profiles was a completely different game.
He could ignore messages consisting of bad one-liners. He responded to the ones that showed a sense of humor or displayed something interesting in their bios. Back when he was the pursuer, he'd swapped three to five messages to get a single date.
Now he'd send just one reply. Want to meet?
If there were a different number of frogs, how many should she kick into the pond before looking for one worth kissing? From failures in math, funny jokes, cool facts, puzzles, comics… Updated Mon-Fri! Most Helpful Opinion mho Rate. Forget the real world, and try to understand what the maths tells you. Learn more Select as Most Helpful Opinion? Is there anything about the real process of partner choice that we have left out? I personally other feel differently so this shouldn't be applied universallyin a healthy separation between my personal and professional lives, and if a date wanted to impress me with math, I wondered if they might continue to do so if our relationship became more serious and that became a serious deterrent to being involved with them. We're meant to be doing some Dating a mathematician here, so by "standards" I mean that for each frog there is a number, called a decision number, Dating a mathematician, below which the princess shouldn't kiss Dating a mathematician here's how to calculate the decision number. Unless you're an incurable romantic who thinks that there's just one perfect person out there for you, you can be very happy with frog number 2, Dating a mathematician. Avoid, on the whole, mathematicians… I am an expert on NOT avoiding mathematicians:
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