Students Share Research Journeys at Bass Connections Showcase

From the highlands of north central Peru to high schools in North Carolina, student researchers in Duke’s Bass Connections program are gathering data in all sorts of unique places.

As the school year winds down, they packed into Duke’s Scharf Hall last week to hear one another’s stories.

Students and faculty gathered in Scharf Hall to learn about each other’s research at this year’s Bass Connections showcase. Photo by Jared Lazarus/Duke Photography.

The Bass Connections program brings together interdisciplinary teams of undergraduates, graduate students and professors to tackle big questions in research. This year’s showcase, which featured poster presentations and five “lightning talks,” was the first to include teams spanning all five of the program’s diverse themes: Brain and Society; Information, Society and Culture; Global Health; Education and Human Development; and Energy.

“The students wanted an opportunity to learn from one another about what they had been working on across all the different themes over the course of the year,” said Lori Bennear, associate professor of environmental economics and policy at the Nicholas School, during the opening remarks.

Students seized the chance, eagerly perusing peers’ posters and gathering for standing-room-only viewings of other team’s talks.

The different investigations took students from rural areas of Peru, where teams interviewed local residents to better understand the transmission of deadly diseases like malaria and leishmaniasis, to the North Carolina Museum of Art, where mathematicians and engineers worked side-by-side with artists to restore paintings.

Machine learning algorithms created by the Energy Data Analytics Lab can pick out buildings from a satellite image and estimate their energy consumption. Image courtesy Hoël Wiesner.

Students in the Energy Data Analytics Lab didn’t have to look much farther than their smart phones for the data they needed to better understand energy use.

“Here you can see a satellite image, very similar to one you can find on Google maps,” said Eric Peshkin, a junior mathematics major, as he showed an aerial photo of an urban area featuring buildings and a highway. “The question is how can this be useful to us as researchers?”

With the help of new machine-learning algorithms, images like these could soon give researchers oodles of valuable information about energy consumption, Peshkin said.

“For example, what if we could pick out buildings and estimate their energy usage on a per-building level?” said Hoël Wiesner, a second year master’s student at the Nicholas School. “There is not really a good data set for this out there because utilities that do have this information tend to keep it private for commercial reasons.”

The lab has had success developing algorithms that can estimate the size and location of solar panels from aerial photos. Peshkin and Wiesner described how they are now creating new algorithms that can first identify the size and locations of buildings in satellite imagery, and then estimate their energy usage. These tools could provide a quick and easy way to evaluate the total energy needs in any neighborhood, town or city in the U.S. or around the world.

“It’s not just that we can take one city, say Norfolk, Virginia, and estimate the buildings there. If you give us Reno, Tuscaloosa, Las Vegas, Pheonix — my hometown — you can absolutely get the per-building energy estimations,” Peshkin said. “And what that means is that policy makers will be more informed, NGOs will have the ability to best service their community, and more efficient, more accurate energy policy can be implemented.”

Some students’ research took them to the sidelines of local sports fields. Joost Op’t Eynde, a master’s student in biomedical engineering, described how he and his colleagues on a Brain and Society team are working with high school and youth football leagues to sort out what exactly happens to the brain during a high-impact sports game.

While a particularly nasty hit to the head might cause clear symptoms that can be diagnosed as a concussion, the accumulation of lesser impacts over the course of a game or season may also affect the brain. Eynde and his team are developing a set of tools to monitor both these impacts and their effects.

A standing-room only crowd listened to a team present on their work “Tackling Concussions.” Photo by Jared Lazarus/Duke Photography.

“We talk about inputs and outputs — what happens, and what are the results,” Eynde said. “For the inputs, we want to actually see when somebody gets hit, how they get hit, what kinds of things they experience, and what is going on in the head. And the output is we want to look at a way to assess objectively.”

The tools include surveys to estimate how often a player is impacted, an in-ear accelerometer called the DASHR that measures the intensity of jostles to the head, and tests of players’ performance on eye-tracking tasks.

“Right now we are looking on the scale of a season, maybe two seasons,” Eynde said. “What we would like to do in the future is actually follow some of these students throughout their career and get the full data for four years or however long they are involved in the program, and find out more of the long-term effects of what they experience.”

Kara J. Manke, PhD

Post by Kara Manke

Visualizing the Fourth Dimension

Living in a 3-dimensional world, we can easily visualize objects in 2 and 3 dimensions. But as a mathematician, playing with only 3 dimensions is limiting, Dr. Henry Segerman laments.  An Assistant Professor in Mathematics at Oklahoma State University, Segerman spoke to Duke students and faculty on visualizing 4-dimensional space as part of the PLUM lecture series on April 18.

What exactly is the 4th dimension?

Let’s break down spatial dimensions into what we know. We can describe a point in 2-dimensional space with two numbers x and y, visualizing an object in the xy plane, and a point in 3D space with 3 numbers in the xyz coordinate system.

Plotting three dimensions in the xyz coordinate system.

While the green right-angle markers are not actually 90 degrees, we are able to infer the 3-dimensional geometry as shown on a 2-dimensional screen.

Likewise, we can describe a point in 4-dimensional space with four numbers – x, y, z, and w – where the purple w-axis is at a right angle to the other regions; in other words, we can visualize 4 dimensions by squishing it down to three.

Plotting four dimensions in the xyzw coordinate system.

One commonly explored 4D object we can attempt to visualize is known as a hypercube. A hypercube is analogous to a cube in 3 dimensions, just as a cube is to a square.

How do we make a hypercube?

To create a 1D line, we take a point, make a copy, move the copied point parallely to some distance away, and then connect the two points with a line.

Similarly, a square can be formed by making a copy of a line and connecting them to add the second dimension.

So, to create a hypercube, we move identical 3D cubes parallel to each other, and then connect them with four lines, as depicted in the image below.

To create an n–dimensional cube, we take 2 copies of the (n−1)–dimensional cube and connecting corresponding corners.

Even with a 3D-printed model, trying to visualize the hypercube can get confusing. 

How can we make a better picture of a hypercube? “You sort of cheat,” Dr. Segerman explained. One way to cheat is by casting shadows.

Parallel projection shadows, depicted in the figure below, are caused by rays of light falling at a  right angle to the plane of the table. We can see that some of the edges of the shadow are parallel, which is also true of the physical object. However, some of the edges that collide in the 2D cast don’t actually collide in the 3D object, making the projection more complicated to map back to the 3D object.

Parallel projection of a cube on a transparent sheet of plastic above the table.

One way to cast shadows with no collisions is through stereographic projection as depicted below.

The stereographic projection is a mapping (function) that projects a sphere onto a plane. The projection is defined on the entire sphere, except the point at the top of the sphere.

For the object below, the curves on the sphere cast shadows, mapping them to a straight line grid on the plane. With stereographic projection, each side of the 3D object maps to a different point on the plane so that we can view all sides of the original object.

Stereographic projection of a grid pattern onto the plane. 3D print the model at Duke’s Co-Lab!

Just as shadows of 3D objects are images formed on a 2D surface, our retina has only a 2D surface area to detect light entering the eye, so we actually see a 2D projection of our 3D world. Our minds are computationally able to reconstruct the 3D world around us by using previous experience and information from the 2D images such as light, shade, and parallax.

Projection of a 3D object on a 2D surface.

Projection of a 4D object on a 3D world

How can we visualize the 4-dimensional hypercube?

To use stereographic projection, we radially project the edges of a 3D cube (left of the image below) to the surface of a sphere to form a “beach ball cube” (right).

The faces of the cube radially projected onto the sphere.

Placing a point light source at the north pole of the bloated cube, we can obtain the projection onto a 2D plane as shown below.

Stereographic projection of the “beach ball cube” pattern to the plane. View the 3D model here.

Applied to one dimension higher, we can theoretically blow a 4-dimensional shape up into a ball, and then place a light at the top of the object, and project the image down into 3 dimensions.

Left: 3D print of the stereographic projection of a “beach ball hypercube” to 3-dimensional space. Right: computer render of the same, including the 2-dimensional square faces.

Forming n–dimensional cubes from (n−1)–dimensional renderings.

Thus, the constructed 3D model of the “beach ball cube” shadow is the projection of the hypercube into 3-dimensional space. Here the 4-dimensional edges of the hypercube become distorted cubes instead of strips.

Just as the edges of the top object in the figure can be connected together by folding the squares through the 3rd dimension to form a cube, the edges of the bottom object can be connected through the 4th dimension

Why are we trying to understand things in 4 dimensions?

As far as we know, the space around us consists of only 3 dimensions. Mathematically, however, there is no reason to limit our understanding of higher-dimensional geometry and space to only 3, since there is nothing special about the number 3 that makes it the only possible number of dimensions space can have.

From a physics perspective, Einstein’s theory of Special Relativity suggests a connection between space and time, so the space-time continuum consists of 3 spatial dimensions and 1 temporal dimension. For example, consider a blooming flower. The flower’s position it not changing: it is not moving up or sideways. Yet, we can observe the transformation, which is proof that an additional dimension exists. Equating time with the 4th dimension is one example, but the 4th dimension can also be positional like the first 3. While it is possible to visualize space-time by examining snapshots of the flower with time as a constant, it is also useful to understand how space and time interrelate geometrically.

Explore more in the 4th dimension with Hypernom or Dr. Segerman’s book “Visualizing Mathematics with 3D Printing“!

Post by Anika Radiya-Dixit.

 

 

Hidden No More: Women in STEM reflect on their Journeys

Back when she was a newly-minted Ph.D., Ayana Arce struggled to picture her future life as an experimental physicist. An African American woman in a field where the number of black women U.S. doctorates is still staggeringly small, Arce could not identify many role models who looked like her.

“I didn’t know what my life would look like as a black postdoc or faculty member,” Arce said.

But in the end, Arce – an associate professor of physics at Duke who went on to join the international team of physicists who discovered the Higgs Boson in 2012 — drew inspiration from her family.

“I looked to the women such as my mother who had had academic careers, and tried to think about how I could shape my life to look something like that, and I realized that it could be something I could make work,” Arce said.

Adrienne Stiff-Roberts, Fay Cobb Payton, Kyla McMullen, Robin Coger and Valerie Ashby on stage at the Hidden Figures No More panel discussion.

Adrienne Stiff-Roberts, Fay Cobb Payton, Kyla McMullen, Robin Coger and Valerie Ashby on stage at the Hidden Figures No More panel discussion. Credit: Chris Hildreth, Duke Photography.

Arce joined five other African American women faculty on the stage of Duke’s Griffith Film Theater March 23 for a warm and candid discussion on the joys and continuing challenges of their careers in science, technology, engineering and math (STEM) fields.

The panel, titled “Hidden Figures No More: Highlighting Phenomenal Women in STEM,” was inspired by Hidden Figures, a film which celebrates three pioneering African American women mathematicians who overcame racial segregation and prejudice to play pivotal roles in NASA’s first manned space flight.

The panel discussion was spearheaded by Johnna Frierson, Director of the Office of Diversity and Inclusion at the Pratt School of Engineering, and co-sponsored by the Duke Women’s Center. It was followed by a free screening of the film.

Though our society has made great strides since the days depicted in the film, women and minorities still remain under-represented in most STEM fields. Those who do pursue careers in STEM must overcome numerous hurdles, including unconscious bias and a lack of colleagues and role models who share their gender and race.

“In my field, at some of the smaller meetings, I am often the only black woman present at the conference, many times I’m the only black person at all,” said Adrienne Stiff-Roberts, an Associate Professor of Electrical and Computer Engineering at Duke. “In that atmosphere often it can be very challenging to engage with others in the way that you are supposed to, and you can feel like an outsider.”

Valerie Ashby and Ayana Arce onstage at the Hidden Figures No More panel discussion

Valerie Ashby and Ayana Arce shared their experiences. Credit: Chris Hildreth, Duke Photography

Stiff-Roberts and the other panelists have all excelled in the face of these challenges, making their marks in fields that include physics, chemistry, computer science, mechanical engineering and electrical engineering. On Thursday they shared their thoughts and experiences with a diverse audience of students, faculty, community members and more than a few kids.

Many of the panelists credited teams of mentors and sponsors for bolstering them when times got tough, and encouraged young scientists to form their own support squads.

Valerie Ashby, Dean at Duke’s Trinity College of Arts and Sciences, advised students to look for supporters who have a vision for what they can become, and are eager to help them get there. “Don’t assume that your help might come from people who you might expect your help to come from,” Ashby said.

The importance of cheerleading from friends, and particularly parents, can never be overestimated, the panelists said.

“Having someone who will celebrate every single positive with you is a beautiful thing,” said Ashby, in response to a mother seeking advice for how to support a daughter majoring in biomedical engineering. “If your daughter is like many of us, we’ll do 99 great things but if we do one wrong thing we will focus on the one wrong thing and think we can’t do anything.”

Women in STEM can also be important and powerful allies to each other, noted Kyla McMullen, an Assistant Professor of Computer and Information Science at the University of Florida.

“I have seen situations where a woman suggests something and then the male next her says the same thing and gets the credit,” McMullen said. “That still happens, but one thing that I see help is when women make an effort to reiterate the points made by other women so people can see who credit should be attributed to.”

With all the advice out there for young people who are striving to succeed in STEM – particularly women and underrepresented minorities – the panelists advocated that everyone to stay true to themselves, above all.

“I want to encourage everyone in the room – whether you are a budding scientist or woman scholar – you can be yourself,” Ashby said. “You should make up in your mind that you are going to be yourself, no matter what.”

Kara J. Manke, PhD

Post by Kara Manke

The Man Who Knew Infinity, and his biggest fan

Ken Ono, a distinguished professor of mathematics at Emory University, was visibly thrilled to be at Duke last Thursday, January 26. Grinning from ear to ear, he announced that he was here to talk about three of his favorite things: math, movies, and “one of the most inspirational figures in my life”: Srinivasa Ramanujan.

Professor Ken Ono of Emory University poses with a bust of Newton and one of Ramanujan’s legendary notebook pages. Source: IFC Films.

Ramanujan, I learned, is one of the giants of mathematics; an incontestable genius, his scrawls in letters and notebooks have spawned whole fields of study, even up to 100 years after his death. His life story continues to inspire mathematicians around the globe—as well as, most recently, a movie which Ono helped produce: The Man Who Knew Infinity, featuring Hollywood stars Dev Patel and Jeremy Irons.

I didn’t realize until much too late that this lecture was essentially one massive spoiler for the movie. Nevertheless, I got to appreciate the brains and the heart behind the operation in hearing Ono express his passion for the man who, at age 16, inspired him to see learning in a new light. Ramanujan’s story follows.

Ramanujan was born in Kambakunam, India in 1887, the son of a cloth merchant and a singer at a local temple. He was visibly gifted from a young age, not only an outstanding student, but also a budding intellectual: by age 13, he had discovered most of modern trigonometry by himself.

Ramanujan’s brilliance earned him scholarships to attend college, only for him to flunk out not once, but twice: he was so engrossed in mathematics that he paid little heed to his actual schoolwork and let his grades suffer. His family and friends, aware of his genius, supported him anyway.

Thus, he spent the daytime in a low-level accounting job that earned him barely enough income to live, and spent the night scribbling groundbreaking mathematics in his notebooks.

A photo portrait of Srinivasa Ramanujan, a brilliant Indian mathematician born in the late 19th century. Source: IFC Films.

Unable to share his discoveries and explain their importance to those around him, Ramanujan finally grew so frustrated that, in desperation, he wrote to dozens of prominent English mathematics professors asking for help. The first of these to respond was G. H. Hardy (for any Biology nerds, this is the Hardy of the Hardy-Weinberg equilibrium), who examined the mathematics Ramanujan included in his letters and was so astounded by what he found that, at first, he thought it was a hoax perpetrated by his friend.

Needless to say, it wasn’t a hoax.

Ramanujan left India to join Hardy in England and publish his discoveries. The meat of the movie, according to Ono, is “the transformation of the relationship between these two characters:” one, a devout Hindu with no formal experience in higher education; the other, a haughty English professor who happened to be an atheist.

The two push past their differences and manage to jointly publish 30 papers based on Ramanujan’s work. Overcoming impossible odds—poverty, World War I, and racism in particular—Ramanujan’s discoveries finally found the light of day.

Sadly, Ramanujan’s story was cut short: a lifelong vegetarian, he fell ill of malnutrition while working in England, returning to India for the last year of his life in the hopes that the warmer climate would improve his health. He died in 1920, at 32 years old.

He continued writing to Hardy from his deathbed, his last letter including revolutionary ideas, which, like much of his work, were so far ahead of his time that mathematicians only began to wrap their minds around them decades after his death.

“Ramanujan was a great anticipator of mathematics, writing formulas that seemed foreign or random at the time but later inspired deep and revolutionary discoveries in math,” Ono said.

Ono’s infatuation with Ramanujan began when he was 16 years old, himself the son of a mathematics professor at Johns Hopkins University. Upon receiving a letter from Ramanujan’s widow, Ono’s father—by Ono’s account, a very stoic, stern man—was brought to tears. Shocked, Ono began to research the origin of the letter, discovering Ramanujan’s story and reaching a turning point in his own life when he realized that there were aspects to learning that were far more important than grades.

That seems to have worked out quite well for Ono, considering his success and expertise in his own area of study—not to mention that he now has “Hollywood producer” under his belt.

Professor Ken Ono chats with actor Dev Patel on the set of The Man Who Knew Infinity. Photo credit: Sam Pressman.

 

Post by Maya Iskandarani

Using the Statistics of Disorder to Unravel Real-World Chaos

What do election polls, hospital records, and the Syrian conflict have in common? How can a hospital use a patient’s vital signs to calculate their risk of cardiac arrest in real time?

Duke statistical science professor Rebecca Steorts

Duke statistical science professor Rebecca Steorts

Statistician Rebecca Steorts is developing advanced data analysis methods to answer these questions and other pressing real-world problems. Her research has taken her from computer science to biostatistics and hospital care to human rights.

One major focus of Steorts’ research has been estimating death counts in the Syrian civil war. She is working with her research group at Duke and the Human Rights Data Analysis Group (https://hrdag.org/) on combining databases of death records into a single master list of deaths in the conflict, a task known as record linkage.

“The key problem of record linkage is this: you have this duplicated information, how do you remove it?” explained Steorts. For example, journalists from different organizations might independently record the same death in their databases. Those duplicates have to be removed before an accurate death toll can be determined.

At first glance, this might seem like an easy task. But typographic errors, missing information, and inconsistent record-keeping make hunting for duplicates a complex and time consuming problem; a simple algorithm would require days to sort through all the records. So Steorts and her collaborators designed software to sift through the different databases using powerful machine learning techniques. In 2015, she was named one of MIT Technology Review’s 35 Innovators Under 35 for her work on the Syrian conflict. She credits a number of colleagues and students for their contributions to the project, including Anshumali Shrivastava (Rice University), Megan Price (HRDAG), Brenda Betancourt and Abbas Zaid (Duke University), Jeff Miller (Harvard Biostatistics, formerly Duke University), Hanna Wallach (Microsoft Research), and Giacomo Zanella (University of Bocconi and Visitor of Duke University in 2016).

Steorts’ work towards estimating death counts in the Syrian conflict is still ongoing, but human rights isn’t the only field that she plans to study. “I think of my work as very interdisciplinary,” she said. “For me, it’s all about the applications.”

Recently, Steorts, colleague Ben Goldstein, and students Reuben McCreanor and Angie Shen have been applying statistical methods to medical data from the Duke healthcare system. Her ultimate goal is to find techniques that can be used for many different applications and data sets.

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Guest post by Angela Deng, North Carolina School of Science and Math, Class of 2017

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