Duke Research Blog

Following the people and events that make up the research community at Duke.

Category: Mathematics (Page 2 of 8)

Would You Expect a ‘Real Man’ to Tweet “Cute” or Not?

There’s nothing cute about stereotypes, but as a species, we seem to struggle to live without them.

In a clever new study led by Jordan Carpenter, who is now a postdoctoral fellow at Duke, a University of Pennsylvania team of social psychologists and computer scientists figured out a way to test just how accurate our stereotypes about language use might be, using a huge collection of real tweets and a form of artificial intelligence called “natural language processing.”

Wordclouds show the words in tweets that raters mistakenly attributed to Female authors (left) or Males (right).

Word clouds show the words in tweets that raters mistakenly attributed to Female authors (left) or Males (right). The larger the word appears, the more often the raters were fooled by it. Word color indicates the frequency of the word; gray is least frequent, then blue, and dark red is the most frequent. <url> means they used a link in their tweet.

Starting with a data set that included the 140-character bon mots of more than 67,000 Twitter users, they figured out the actual characteristics of 3,000 of the authors. Then they sorted the authors into piles using four criteria – male v. female; liberal v. conservative; younger v. older; and education (no college degree, college degree, advanced degree).

A random set of 100 tweets by each author over 12 months was loaded into the crowd-sourcing website Amazon Mechanical Turk. Intertubes users were then invited to come in and judge what they perceived about the author one characteristic at a time, like age, gender, or education, for 2 cents per rating. Some folks just did one set, others tried to make a day’s wage.

The raters were best at guessing politics, age and gender. “Everybody was better than chance,” Carpenter said. When guessing at education, however, they were worse than chance.

Jordan Carpenter is a newly-arrived Duke postdoc working with Walter Sinnott-Armstrong in philosophy and brain science.

Jordan Carpenter is a newly-arrived Duke postdoc working with Walter Sinnott-Armstrong in philosophy and brain science.

“When they saw the word S*** [this is a family blog folks, work with us here] they most often thought the author didn’t have a college degree. But where they went wrong was they overestimated the importance of that word,” Carpenter said. Raters seemed to believe that a highly-educated person would never tweet the S-word or the F-word. Unfortunately, not true! “But it is a road to people thinking you’re not a Ph.D.,” Carpenter wisely counsels.

The raters were 75 percent correct on gender, by assuming women would be tweeting words like Love, Cute, Baby and My, interestingly enough. But they got tricked most often by assuming women would not be talking about News, Research or Ebola or that the guys would not be posting Love, Life or Wonderful.

Female authors were slightly more likely to be liberal in this sample of tweets, but not as much as the raters assumed. Conservatism was viewed by raters as a male trait. Again, generally true, but not as much as the raters believed.

Youthful authors were correctly perceived to be more likely to namedrop a @friend, or say Me and Like and a few variations on the F-bomb, but they could throw the raters for a loop by using Community, Our and Original.

And therein lies the social psychology takeaway from all this: “An accurate stereotype should be one with accurate social judgments of people,” but clearly every stereotype breaks down at some point, leading to “mistaken social judgement,” Carpenter said. Just how much stereotypes should be used or respected is a hot area of discussion within the field right now, he said.

The other value of the paper is that it developed an entirely new way to apply the tools of Big Data analysis to a social psychology question without having to invite a bunch of undergraduates into the lab with the lure of a Starbucks gift card. Using tweets stripped of their avatars or any other identifier ensured that the study was testing what people thought of just the words, nothing else, Carpenter said.

The paper is “Real Men Don’t Say “Cute”: Using Automatic Language Analysis To Isolate Inaccurate Aspects Of Stereotypes.”  You can see the paper in Social Psychology and Personality Science, if you have a university IP address and your library subscribes to Sage journals. Otherwise, here’s a press release from the journal. (DOI: 10.1177/1948550616671998 )

Karl Leif BatesPost by Karl Leif Bates

Diabetes — and Privacy — Meet ‘Big Data’

“Click here to consent forever.”

If consent to participate in medical research were that simple, Joanna Radin of Yale University would have to find a new focus for her research, and I would never have found the Trent Center for Bioethics, Humanities & History of Medicine.

Luckily for us both, this is not the case. Medical consent is a very complex issue that can, as Radin’s research attests, traverse generations.

joanna-radin-headshot

Joanna Radin’s reserach focuses on the intersection of medical history, anthropology and ethics at Yale University. Source: Yale School of Medicine

Radin is an Associate Professor of Medical History at Yale, the perfect fit for the Humanities in Medicine Lecture Series taking place this month at the Trent Center. Her research nails the narrow intersection of medical history, anthropology, bioethics and data analytics. In fact, Radin’s appeal is so broad that her visit to Duke was sponsored by no less than six Duke departments, including the Departments of Computer Science, History, Electrical and Computer Engineering, Cultural Anthropology and Statistical Science.

Radin’s lecture honed in on a well-known case in the realm of bioethics and medical history: the Pima Native American tribe in Arizona, which is known for unusually high rates of diabetes and obesity. The Pima were the first Native American tribe to be granted a reservation in Arizona—30,000 acres—at the beginning of the California Gold Rush. In 1963, following nearly half a century of mass famine among the Pima, the National Institute of Health (NIH) conducted a survey for rheumatoid arthritis in the Pima tribe, instead discovering a frighteningly high frequency of diabetes.

In 1965, the NIH initiated a long-term observational study of the Pima that continued for about 40 years, though it was meant to last no more than 10. The goal of the study was to learn about diabetes in the “natural laboratory” of sorts that the Pima reservation unwittingly provided. The data collected in this study came to be known as the Pima Indian Diabetes Data set (PIDD).

Machine learning enters the story around 1987, when David Aha and colleagues at the University of California, Irvine (UCI) created the UCI Machine Learning Repository, an archive containing thousands of data sets, databases and data generators. The repository is still active today, virtually a gold mine for researchers in machine learning to test their algorithms. The PIDD is one of the oldest data sets on file in the UCI archive, “a standard for testing data mining algorithms for accuracy in predicting diabetes,” according to Radin.

pima_indian_man_miguel_a_farmer_pima_arizona_ca-1900_chs-3625

A Pima farmer in Pima, Arizona, circa 1900. Source: Wikimedia Commons

Generations’ worth of data on the Pima tribe have been publicly accessible in the UCI archive for over two decades, creating ethical controversy around the accessibility of information as personal as blood pressure, body mass index (BMI) and number of pregnancies of Pima Native Americans. Though the PIDD can help refine machine learning algorithms that could accurately predict—and prevent—diabetes, the privacy issues provoked by the publicness of the data are impossible to ignore.

This is where “eternal” medical consent enters the equation: no researcher can realistically inform a study participant of what their medical data will be used for 40 years in the future.

These are the interdisciplinary questions that Radin brought forth in her lecture, weaving together seemingly opposite fields of study in an engaging, thought-provoking presentation. No one who left that room will look at the Apple Terms & Conditions the same way again.

 

Post by Maya Iskandarani iskandarani_maya_100hed

Taking Math Beyond the Blackboard

Most days, math graduate student Veronica Ciocanel spends her time modeling how frog eggs go from jelly-like blobs to tiny tadpoles having a well-defined front and back, top and bottom. But for a week this summer, she used some of the same mathematical tools from her Ph.D. research at Brown to help a manufacturing company brainstorm better ways to filter nasty-smelling pollutants from industrial exhaust fumes.

Math professor Ryan Pellico of Trinity College took a similar leap. Most of his research aims to model suspension bridges that twist and bounce to the point of collapse. But he spent a week trying to help a defense and energy startup devise better ways to detect landmines using ground-penetrating radar.

Ciocanel and Pellico are among more than 85 people from across the U.S., Canada and the U.K. who met at Duke University June 13-17 for a five-day problem-solving workshop for mathematicians, scientists and engineers from industry and academia.

The concept got its start at Oxford University in 1968 and has convened 32 times. Now the Mathematical Problems in Industry workshop (MPI) takes place every summer at a different university around the U.S. This is the first time Duke has hosted the event.

The participants’ first task was to make sense of the problems presented by the companies and identify areas where math, modeling or computer simulation might help.

One healthcare services startup, for example, was developing a smartphone app to help asthma sufferers and their doctors monitor symptoms and decide when patients should come in for care. But the company needed additional modeling and machine learning expertise to perfect their product.

Another company wanted to improve the marketing software they use to schedule TV ads. Using a technique called integer programming, their goal was to ensure that advertisers reach their target audiences and stay within budget, while also maximizing revenue for the networks selling the ad time.

“Once we understood what the company really cared about, we had to translate that into a math problem,” said University of South Carolina graduate student Erik Palmer. “The first day was really about listening and letting the industry partner lead.”

Mathematicians Chris Breward of the University of Oxford and Sean Bohun of the University of Ontario Institute of Technology were among more than 80 people who met at Duke in June for a week-long problem-solving workshop for scientists and engineers from industry and academia.

Mathematicians Chris Breward of the University of Oxford and Sean Bohun of the University of Ontario Institute of Technology were among more than 80 people who met at Duke in June for a week-long problem solving workshop for scientists and engineers from industry and academia.

For the rest of the week, the participants broke up into teams and fanned out into classrooms scattered throughout the math and physics building, one classroom for each problem. There they worked for the next several days, armed with little more than caffeine and WiFi.

In one room, a dozen or so faculty and students sat in a circle of desks in deep concentration, intently poring over their laptops and programming in silence.

Another team paced amidst a jumble of power cords and coffee cups, peppering their industry partner with questions and furiously scribbling ideas on a whiteboard.

“Invariably we write down things that turn out later in the week to be completely wrong, because that’s the way mathematical modeling works,” said University of Oxford math professor Chris Breward, who has participated in the workshop for more than two decades. “During the rest of the week we refine the models, build on them, correct them.”

Working side by side for five days, often late into the night, was intense.

“It’s about learning to work with people in a group on math and coding, which are usually things you do by yourself,” Ciocanel said.

“By the end of the week you’re drained,” said math graduate student Ann Marie Weideman of the University of Maryland, Baltimore County.

For Weideman, one of the draws of the workshop was the fresh input of new ideas. “Everyone comes from different universities, so you get outside of your bubble,” she said.

“Here people have tons of different approaches to problems, even for things like dealing with missing data, that I never would have thought of,” Weideman added. “If I don’t know something I just turn to the person next to me and say, ‘hey, do you know how to do this?’ We’ve been able to work through problems that I never could have solved on my own in a week’s worth of time.”

Supported by funding from the National Science Foundation and the industry partners, the workshop attracts a wide range of people from math, statistics, biostatistics, data science, computer science and engineering.

monday_groupMore than 50 graduate students participated in this year’s event. For them, one of the most powerful parts of the workshop was discovering that the specialized training they received in graduate school could be applied to other areas, ranging from finance and forensics to computer animation and nanotechnology.

“It’s really cool to find out that you have some skills that are valuable to people who are not mathematicians,” Pellico said. “We have some results that will hopefully be of value to the company.”

On the last day of the workshop, someone from each group presented their results to their company partner and discussed possible future directions.

The participants rarely produce tidy solutions or solve all the problems in a week. But they often uncover new avenues that might be worth exploring, and point to new approaches to try and questions to ask.

“We got lots of new ideas,” said industry representative Marco Montes de Oca, whose company participated in the MPI workshop for the second time this year. “This allows us to look at our problems with new eyes.”

Next year’s MPI workshop will be held at the New Jersey Institute of Technology in Newark.

Robin SmithPost by Robin A. Smith

Post-Game Roundup from the Brain Teaser Bowl

Duke claims another top ten finish in North America’s most prestigious math competition

DURHAM, N.C. — The Blue Devils may have lost in the Sweet 16 during March Madness 2016, but a Duke team crushed more than 500 other schools in the NCAA tournament of the math world, known by mathletes as the Putnam, claiming a top ten finish for the 22nd time since 1990.

Left to right, Trung Can, Feng Gui, Professor David Kraines, Tony Qiao and Alex Milu are pictured in front of the Math/Physics Building. Can, Gui, Qiao and Milu are the top four Duke finishers in the annual Putnam Competition. Their combined rankings carried Duke to a tenth place finish overall. Photo by Megan Mendenhall, Duke Photography.

Left to right, Trung Can, Feng Gui, Professor David Kraines, Tony Qiao and Alex Milu are pictured in front of the Math/Physics Building. Can, Gui, Qiao and Milu are the top four Duke finishers in the annual Putnam Competition. Their combined rankings carried Duke to a tenth place finish overall. Photo by Megan Mendenhall, Duke Photography.

Alex Milu ’16, Tony Qiao ’17, Trung Can ’18 and Feng Gui ’18 scored higher than 90 percent of the 4,275 undergraduates who competed in this year’s event. More than a dozen other Duke students also competed in this year’s contest. The results of the 76th annual competition were announced this month.

Named after an 1882 Harvard graduate, the William Lowell Putnam Mathematical Competition is the most prestigious college-level math contest you have probably never heard of.

Every year on the first Saturday in December, thousands of students from across the U.S. and Canada compete in a grueling six-hour exam to see who can be the Steph Curry of math.

Contestants in the annual Putnam Competition have six hours to solve 12 problems.

Contestants in the annual Putnam Competition have six hours to solve 12 problems.

Armed with nothing more than pencil and paper, their task is to solve 12 brain bending math problems. No laptops, no course notes.

“These are not problems that textbook learning will help you much with,” said associate professor of mathematics David Kraines, who has coached Duke’s Putnam teams for much of the past 25 years.

“Knowing anything beyond calculus or linear algebra is really not a help,” Kraines said. Instead, coming up with solutions requires an “ability to think abstractly and outside the box.”

“We have A+ students who don’t do well at all in this competition, and others who don’t get great grades for one reason or another, and who become Putnam stars,” Kraines said.

“You have to think more creatively than you do in class,” said Feng Gui, who finished among the top 8 percent and competed in similar competitions as a high school student in China.

One question gave the sequence of numbers 6,16,27,36…, and asked the competitor to prove or disprove that there is some number in the sequence whose base 10 representation ends with 2015.

“Most of the questions don’t have numerical answers,” Kraines said. “They say ‘prove this,’ or ‘show that.’ To do well you have to justify your solution mathematically.”

A perfect score on the 12-question test is 120 points, but the grading is so tough that almost two thirds of this year’s Putnam contestants got zero points. Only one in five contestants correctly solved even one problem.

“It was a little tougher than usual,” said Alex Milu of Bucharest, Romania, a Karsh Scholar who took the Putnam for the fourth time this year and was named Honorable Mention for scoring in the top two percent, or 54th out of 4,275 students.

Calculators wouldn't have been much help in tackling the test questions from this year's Putnam Competition.

Calculators wouldn’t have been much help in tackling the test questions from this year’s Putnam Competition.

For Trung Can, a former gold medalist from Vietnam in the annual International Math Olympiad (IMO), the world math championship for high schoolers, math competitions like the Putnam are an opportunity to “meet people who share the same passion. Those friendships can last a lifetime,” said Can, who will help lead a training camp for high school students in Vietnam this July.

The Blue Devils competed sporadically in the Putnam in the 1970s and 80s, but Duke’s first top ten win was in 1990, when a three-person Duke team finished in second place behind Harvard.

That year, Kraines persuaded the department to start offering a half-credit problem-solving seminar in the fall to prepare students for the competition. Each week they focus on a different topic. One week it might be number theory, the next week geometry or combinatorics the week after that. “We entice them with pizza,” Kraines said.

Around the same time, Duke also started making a concerted effort to attract top math students the same way college sports recruiters attract basketball stars.

“I was able to get on the scholarship committee and we started actively recruiting,” Kraines said. “It worked. We got some fantastic kids.”

What followed was a 15-year run of near-continuous top three finishes. Since 1990, Duke Putnam teams have ranked No. 1 in North America three times, No. 2 twice, and No. 3 six times.

Duke’s Putnam champs don’t burn benches to mark major victories, but they do celebrate in other ways.

Hanging proudly in the math department lounge are some of the retired jerseys of the five Duke students who have placed among the top five highest-ranking individual finishers, known as “Putnam Fellows,” a distinction shared by several Fields Medal winners and Nobel laureates in physics.

The No. 2 jersey of 2002 Putnam winner Melanie Wood is among them, a reminder of the last time a Duke student finished among the top five individual spots.

A scrapbook in Kraines’ office contains dozens of newspaper clippings and other keepsakes from Duke’s earliest wins, including a congratulatory letter from former NC Governor Jim Hunt.

Kraines plans to retire from teaching next year after 45 years at Duke, but this won’t be his last Putnam. “It’s been a very good experience. I don’t plan to leave,” Kraines said.

 

Post by Robin A. Smith Robin Smith

 

The Art of Asking Questions at DataFest 2016

During DataFest, students engaged in intense collaboration. Image courtesy of Rita Lo.

Students engaged in intense collaboration during DataFest 2016, a stats and data analysis competition held from April 1-3 at Duke. Image courtesy of Rita Lo.

On Saturday night, while most students were fast asleep or out partying, Duke junior Callie Mao stayed up until the early hours of the morning pushing and pulling a real-world data set to see what she could make of it — for fun. Callie and her team had planned for months in advance to take part in DataFest 2016, a statistical analysis competition that occurred from April 1 to April 3.

A total of 277 students, hailing from schools as disparate as Duke, UNC Chapel Hill, NCSU, Meredith College, and even one high school, the North Carolina School of Science and Mathematics, gathered in the Edge to extract insight from a mystery data set. The camaraderie was palpable, as students animatedly sketched out their ideas on whiteboard walls and chatted while devouring mountains of free food.

Callie Mao ponders which aspects of data to include in her analysis.

Duke junior Callie Mao ponders which aspects of the data to include in her analysis.

Callie observed that the challenges the students faced at DataFest were extremely unique: “The most difficult part of DataFest is coming up with an idea. In class, we get specific problems, but at DataFest, we are thrown a massive data set and must figure out what to do with it. We originally came up with a lot of ideas, but the data set just didn’t have enough information to fully visualize though.”

At the core, Callie and her team, instead of answering questions posed in class, had to come up with innovative and insightful questions to pose themselves. With virtually no guidance, the team chose which aspects of the data to include and which to exclude.

Another principal consideration across all categories was which tools to use to quickly and clearly represent the data. Callie and her team used R to parse the relevant data, converted their desired data into JSON files, and used D3, a Javascript library, to code graphics to visualize the data. Other groups, however, used Tableau, a drag and drop interface that provided an expedited method for creating beautiful graphics.

Mentors assisted participants with formulating insights and presenting their results

Mentors assisted participants with formulating insights and presenting their results. Image courtesy of Rita Lo.

On Sunday afternoon, students presented their findings to their attentive peers and to a panel of judges, comprised of industry professionals, statistics professors from various universities, and representatives from Data and Visualization Services at Duke Libraries. Judges commended projects based on aspects such as incorporation of other data sources, like Google Adwords, comprehensibility of the data presentation, and the applicability of findings in a real industry setting.

Students competed in four categories:  best use of outside data, best data insight, best visualization, and best recommendation. The Baeesians, pictured below, took first place in best outside data, the SuperANOVA team won best data insight, the Standard Normal team won best visualization, and the Sample Solution team won best recommendation. The winning presentations will be available to view by May 2 at http://www2.stat.duke.edu/datafest/.

Bayesian, the winner of the Best Outside Data category

The Baeasians, winner of the Best Outside Data category at DataFest 2016: Rahul Harikrishnan, Peter Shi, Qian Wang, Abhishek Upadhyaya. (Not pictured Justin Wang) Image courtesy of Rita Lo.

 

By student writer Olivia Zhu  professionalpicture

Why care about the gender gap in science and tech?

A day on the job for Christine McKinley

A day on the job for Christine McKinley

Scenes like the one above are engineer Christine McKinley’s favorite views of the construction sites where she manages building designs and contracts with other engineers. McKinley, a mechanical engineer, musician, and author, enjoys the complexities, high stakes and surprises of her job. Engineers, she says, “design against [surprises] but live for surprises.”

One of these surprises, McKinley told an audience last Thursday Feb. 25 in the Nelson Music Room at Duke, was a talk she had with the director of a community college district. He told her “women aren’t as good as math and science.” Shocked and disappointed that a man in charge of the education of the young students would believe this, McKinley pointed out that several of her accomplished colleagues were women. McKinley, like many other women, was frustrated that she has to work harder than men to get a promotion.

Is this changing? Are women today more prevalent in engineering fields than they were twenty to thirty years ago?

The chart below depicts the distribution of engineers in 1989: only 15 percent are women.

Distribution of Engineering Graduates in 1989

Of course, 1989 was 27 years ago and a different cultural time, with Nintendo’s Game Boy and Prince William’s seventh birthday. But the chart below shows how little those numbers have changed.

Distribution of Engineering Graduates in 2015

For mechanical engineers, the gap is much larger: only 7 percent are women (yellow faces), while the blue faces represent men, with the some frowning ones unhappy to be working with the women.

Percent of female mechanical engineers

Percent of female mechanical engineers

When the workers are broken down into teams, according to McKinley, the image below is what it actually feels like to be working as a female mechanical engineer.

What it actually feels like to be a female mechanical engineer

What it actually feels like to be a female mechanical engineer

Let’s start with the most troubling issue regarding the lack of diversity in engineering. If women and people of color are told that we are not good at math and science, and we believe it, then we are choosing a form of helplessness. Specifically, if we don’t pick apart the data and challenge those who made up this story, then it sticks, and the “rumor” becomes a narrative – and that’s dangerous, McKinley said. However, everyone needs to know basic chemistry, math, and physics to participate in conversations about topics such as medicine, NASA, one’s cholesterol level, and energy conservation as a knowledgeable adult. People need to be STEM-literate to be able to analyze this data, and men, especially in the 1950s, didn’t want women to research the facts and prove a competition.

Why should we care about women choosing careers in STEM fields?

Reason 1: Gender financial inequity: STEM grads make more than non-STEM grads

If we care about the gender pay gap, and only 19 percent of engineering graduates are women, then that aggravates the situation. This gender inequity can be addressed – partly – by women choosing to study engineering, McKinley said.

Of course, money is not the only thing in life; we want jobs with meaning, she added. However, even civil engineers understand that they are in a helping profession, always excited to build a new bridge, for example, to help people cross a flooded river. At the same time, money gives one the ability to leave a spouse, to take care of a disabled child, to find a better job, to afford healthier food; making real money gives one a way to become independent and make better choices. Working a job, however, does not imply that we must “sacrifice [our] life and fun.” McKinley enjoys what she does and has a lot of fun on the job; studying math and science, she says, is not that complex with the right motivation and support.

Reason 2: Humanity’s Survival

A coronal mass ejection (CME) is an enormous eruption of gas and magnetic field that launches billions of tons of plasma from the sun’s surface into space. Such an event occurred in 1859. As a result, farmers plowing field with horses noticed a bright flash of light, steam engines continued to run on schedule, and telegraph operators were confused when their telegraph batteries stopped working. Overall, there were few problems due to the limited technology at the time.

Imagine a CME happening today. All our large pieces of equipment – power stations, transformers, and transmission lines – would get fried.

Equipment involved in the transportation of energy from power plants to users

If these power houses blow up, what are we going to do? With three-year lead-time and $2 trillion cost, they will not be repaired in time for us to continue our daily functions. We now have a civilization-changing event on our hands – what Hurricane Katrina gave us, but now, for entire countries. We are in a time where our dependence on technology is constantly rising – until it’s not. In such a disastrous scenario, we will need more engineers. At this time, everyone – men and women – will come together to work on simple, elegant solutions to make the world better.

Currently, we have a mass shortage of engineers, so those today are overbooked with work. If these engineers are unable to find time to think through the entire solution and review all possible sources of error, then it creates a problem not only for engineering but also for the entire world in general. We are in need of good engineers and a diverse workforce to bring together all our ideas for a better world.

McKinley notes that she finds herself more comfortable when there are other women in the room. As a result, the whole team gets more relaxed, “elevating everyone’s game,” and people get more creative and feel more secure in sharing their ideas.

Grace Hopper created the computers advertised in this flyer.

Grace Hopper created the computers advertised in this flyer.

 

Reason 3: The third reason we care about this view about engineering is the history of STEM achievements by women being ignored or the credit being taken by men.

Women who became mathematicians in the 1900s had to fight hard to have their contributions to the field recognized. The world misses out significantly if the achievements of half of humanity are ignored.

Hertha Aryton was a brilliant mathematician who had been elected the first female member of the Institution of Electrical Engineers in 1899. In 1902, she became the first woman nominated a Fellow of the Royal Society of London. “Because she was married, however,” McKinley quoted, “legal counsel advised that the charter of the Royal Society did not allow the Society to elect her to this distinction.”

Amalie Noether was another incredible mathematician who invented a theorem that united symmetry in nature and the universal laws of conservation. Some consider Noether’s theorem, as it is now called, to be as important as Albert Einstein’s theory of relativity. Einstein himself regarded her as most “significant” and “creative” female mathematician of all time. However, McKinley tells the audience, she was denied a working position at universities simply because they did not hire female professors.

In the 1900’s, more than 1000 women joined an organization called Women Airforce Service Pilots. They transported newly-made planes to the fighter pilots; however, many of the planes were untested, causing 38 of them to die in service. While they went through intense military training and had prior experience, the women were considered “civilian volunteers” and had to fight to be recognized. Further, most of the accepted women to the organization were white, and the only African American applicant was asked to withdraw her application.

Nancy Fitzroy was American engineer and heat transfer expert in the 1900s. She received plenty of criticism as well, but she said it didn’t affect her: “The reaction I pretty much have gotten most of my life is ‘little girl, what are you doing here?’ but I was a good engineer. That’s what made all the difference.”

 

Curiosity, inventiveness, and the urge to improve are not male traits. They are human traits. Women are half of humanity; they are not the spectators. Women must step up and contribute even if it is more difficult. Constantly underestimated as a female mechanical engineer, McKinley says she uses this underestimation as fuel to work harder and become better.

Being an engineer is worth it. Ask great questions, and be really good.

Remember, McKinley told her audience, that engineering is full of surprises. And for people who underestimate you, you’ll be that surprise.

 

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C

Christine McKinley gave her talk in the Nelson Music Building at Duke last Thursday for Feminist/Women’s month.

Christine McKinley is a mechanical engineer, musician, and author. Her musical Gracie and the Atom, won a Portland Drammy for Original Score. Her book Physics for Rock Stars was published in 2014 by Penguin Random House. Christine hosted Brad Meltzer’s Decoded on History Channel and Under New York on Discovery Channel.

You can view her website, read her book, or contact her via email.

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Anika_RD_hed100_2 By Anika Radiya-Dixit

 

 

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