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

Students exploring the Innovation Co-Lab

Author: Nina Cervantes

What is a Model?

When you think of the word “model,” what do you think?

As an Economics major, 
the first thing that comes to my mind is a statistical model, modeling phenomena such as the effect of class size on student test scores. A
car connoisseur’s mind might go straight to a model of their favorite vintage Aston
Martin. Someone else studying fashion even might imagine a runway model. The point is, the term “model” is used in popular discourse incredibly frequently, but are we even sure what it implies?

Annabel Wharton, a professor of Art, Art History, and Visual Studies at Duke, gave a talk entitled “Defining Models” at the Visualization Friday Forum. The forum is a place “for faculty, staff and students from across the university (and beyond Duke) to share their research involving the development and/or application of visualization methodologies.” Wharton’s goal was to answer the complex question, “what is a model?”

Wharton began the talk by defining the term “model,” knowing that it can often times be rather ambiguous. She stated the observation that models are “a prolific class of things,” from architectural models, to video game models, to runway models. Some of these types of things seem unrelated, but Wharton, throughout her talk, pointed out the similarities between them and ultimately tied them together as all being models.

The word “model” itself has become a heavily loaded term. According to Wharton, the dictionary definition of “model” is 9 columns of text in length. Wharton then stressed that a model “is an autonomous agent.” This implies that models must be independent of the world and from theory, as well as being independent of their makers and consumers. For example, architecture, after it is built, becomes independent of its architect.

Next, Wharton outlined different ways to model. They include modeling iconically, in which the model resembles the actual thing, such as how the video game Assassins Creed models historical architecture. Another way to model is indexically, in which parts of the model are always ordered the same, such as the order of utensils at a traditional place setting. The final way to model is symbolically, in which a model symbolizes the mechanism of what it is modeling, such as in a mathematical equation.

Wharton then discussed the difference between a “strong model” and a “weak model.” A strong model is defined as a model that determines its weak object, such as an architect’s model or a runway model. On the other hand, a “weak model” is a copy that is always less than its archetype, such as a toy car. These different classifications include examples we are all likely aware of, but weren’t able to explicitly classify or differentiate until now.

Wharton finally transitioned to discussing one of her favorite models of all time, a model of the Istanbul Hagia Sophia, a former Greek Orthodox Christian Church and later imperial mosque.(See Also: https://muze.gen.tr/muze-detay/ayasofya)

She detailed how the model that provides the best sense of the building without being there is found in a surprising place, an Assassin’s Creed video game. This model is not only very much resembles the actual Hagia Sophia, but is also an experiential and immersive model. Wharton joked that even better, the model allows explorers to avoid tourists, unlike in the actual Hagia Sophia.

Wharton described why the Assassin’s Creed model is a highly effective agent. Not only does the model closely resemble the actual architecture, but it also engages history by being surrounded by a historical fiction plot. Further, Wharton mentioned how the perceived freedom of the game is illusory, because the course of the game actually limits players’ autonomy with code and algorithms.

After Wharton’s talk, it’s clear that models are definitely “a prolific class of things.” My big takeaway is that so many thing in our everyday lives are models, even if we don’t classify them as such. Duke’s East Campus is a model of the University of Virginia’s campus, subtraction is a model of the loss of an entity, and an academic class is a model of an actual phenomenon in the world. Leaving my first Friday Visualization Forum, I am even more positive that models are powerful, and stretch so far beyond the statistical models in my Economics classes.


By Nina Cervantes

On a Mission to Increase Exercise

Dr. Zachary Zenko of the Center for Advanced Hindsight at Duke is on a mission to get people to exercise. He shared this mission and his research aimed at achieving this mission at Duke’s Exercise and the Brain Symposium on December 1st.

Dr. Zenko started out with a revealing statistic: Only 1 in 10 people meet the United States activity guidelines for exercise. While I wasn’t completely shocked at this fact as the U.S. is known for its high rates of obesity and easy access to fast-food, I was definitely eager to learn more about how Dr. Zenko planned to fight this daunting statistic.

Next, Dr. Zenko pointed out a common assumption in exercise psychology: if people know how good exercise is for them, they will exercise. However, this hasn’t proven to be true. Most people already know that they should be exercising, but don’t. And those that do often quickly drop out.

Then Dr. Zenko began to break down Dual-Process Theory in Behavioral Economics. Type 1 Processes are those that are fast and non-conscious, while Type 2 Processes are those that are controlled and conscious. While research on exercise usually focuses on Type 2 processes, Zenko believes that we must focus on both.

Ideally, exercise would involve the affect heuristic, which is a mental shortcut in which an emotional response drives an individual. This heuristic involves Type 1 processes. Dr. Zenko’s goal was to shift away from only considering Type 2 processes, and instead focus on using Type 1 processes to make exercise more appealing.

How did he propose doing this? By continually decreasing the difficulty of exercise. By changing the slope of the intensity of a workout and having a continually declining heart rate, exercisers could have a more pleasant experience. In addition, this positive experience could influence memory and make an individual more likely to exercise in the future.

Dr. Zenko put this hypothesis to the test by having unfit adults exercise while continually decreasing the intensity throughout the workout. While test subjects exercised, he measured the amount of pleasure experienced by asking “How do you feel right now?” at certain intervals. This new exercise method  has the most potential when starting at the highest intensity levels because it leaves more room to change the slope of the workout intensity throughout, leading to an overall more pleasurable workout.

Looking forward, this new method of exercise could possibly change the way we think about exercise. It may not only involve doing the right amount of exercise, but also doing the right kind of exercise that leaves us more likely to exercise in the future. Considering that traditional methods of promoting exercise, such as educating people about its benefits, have not been particularly successful thus far, Dr. Zenko’s method is very exciting.

Dr. Zenko wrapped up his talk by suggesting that people consider exercise prescriptions that are safe, effective, pleasant and enjoyable. As exercise has become a huge part of my weekly routine throughout college, I will definitely take this advice to heart. Maybe even look out for me lowering my intensity during a workout soon in a gym near you?

By Nina Cervantes

Generating Winning Sports Headlines

What if there were a scientific way to come up with the most interesting sports headlines? With the development of computational journalism, this could be possible very soon.

Dr. Jun Yang is a database and data-intensive computing researcher and professor of Computer Science at Duke. One of his latest projects is computational journalism, in which he and other computer science researchers are considering how they can contribute to journalism with new technological advances and the ever-increasing availability of data.

An exciting and very relevant part of his project is based on raw data from Duke men’s basketball games. With computational journalism, Yang and his team of researchers have been able to generate diverse player or team factoids using the statistics of the games.

Grayson Allen headed for the hoop.

Grayson Allen headed for the hoop.

An example factoid might be that, in the first 8 games of this season, Duke has won 100% of its games when Grayson Allen has scored over 20 points. While this fact is obvious, since Duke is undefeated so far this season, Yang’s programs will also be able to generate very obscure factoids about each and every player that could lead to unique and unprecedented headlines.

While these statistics relating player and team success can only imply correlation, and not necessarily causation, they definitely have potential to be eye-catching sports headlines.

Extracting factoids hasn’t been a particularly challenging part of the project, but developing heuristics to choose which factoids are the most relevant and usable has been more difficult.

Developing these heuristics so far has involved developing scoring criteria based on what is intuitively impressive to the researcher. Another possible measure of evaluating the strength of a factoid is ranking the types of headlines that are most viewed. Using this method, heuristics could, in theory, be based on past successes and less on one researcher’s human intuition.

Something else to consider is which types of factoids are more powerful. For example, what’s better: a bolder claim in a shorter period of time, or a less bold claim but over many games or even seasons?

The ideal of this project is to continue to analyze data from the Duke men’s basketball team, generate interesting factoids, and put them on a public website about 10-15 minutes after the game.

Looking forward, computational journalism has huge potential for Duke men’s basketball, sports in general, and even for generating other news factoids. Even further, computational journalism and its scientific methodology might lead to the ability to quickly fact-check political claims.

Right now, however, it is fascinating to know that computer science has the potential to touch our lives in some pretty unexpected ways. As our current men’s basketball beginning-of-season winning streak continues, who knows what unprecedented factoids Jun Yang and his team are coming up with.

By Nina Cervantes

Opportunities at the Intersection of Technology and Healthcare

What’d you do this Halloween?

I attended a talk on the intersection of technology and healthcare by Dr. Erich Huang, who is an assistant professor of Biostatistics & Bioinformatics and Assistant Dean for Biomedical Informatics. He’s also the new co-director of Duke Forge, a health data science research group.

This was not a conventional Halloween activity by any means, but I felt lucky to be exposed to this impactful research surrounded by views of the Duke forest in fall in Penn Pavilion at IBM-Duke Day.

Erich Huang

Erich Huang, M.D., PhD. is the co-director of Duke Forge, our new health data effort.

Dr. Huang began his talk with a statistic: only six out of 53 landmark cancer biology research papers are reproducible. This fact was shocking (and maybe a little bit scary?), considering  that these papers serve as the foundation for saving cancer patients’ lives. Dr. Huang said that it’s time to raise standards for cancer research.

What is his proposed solution? Using data provenance, which is essentially a historical record of data and its origins, when dealing with important biomedical data.

He mentioned Duke Data Service (DukeDS), which is an information technology service that features data provenance for scientific workflows. With DukeDS, researchers are able to share data with approved team members across campus or across the world.

Next, Dr. Huang demonstrated the power of data science in healthcare by describing an example patient. Mr. Smith is 63 years old with a history of heart attacks and diabetes. He has been having trouble sleeping and his feet have been red and puffy. Mr. Smith meets the criteria for heart failure and appropriate interventions, such as a heart pump and blood thinners.

A problem that many patients at risk of heart failure face is forgetting to take their blood thinners. Using Pillsy, a company that makes smart pill bottles with automatic tracking, we could record Mr. Smith’s medication taking and record this information on the blockchain, or by storing blocks of information that are linked together so that each block points to an older version of that information. This type of technology might allow for the recalculation of dosage so that Mr. Smith could take the appropriate amount after a missed dose of a blood thinner.

These uses of data science, and specifically blockchain and data provenance, show great opportunity at the intersection of technology and healthcare. Having access to secure and traceable data can lead to research being more reproducible and therefore reliable.

At the end of his presentation, Dr. Huang suggested as much collaboration in research between IBM and Duke as possible, especially in his field. Seeing that the Research Triangle Park location of IBM is the largest IBM development site in the world and is conveniently located to one of the best research universities in the nation, his suggestion makes complete sense.

By Nina Cervantes        

New Blogger Nina Cervantes: Economics Senior from the Sunny State

Hi all!  My name is Nina Cervantes and I’m a senior economics major at Duke also pursuing a certificate in markets and management studies and a minor in history. I’m from a town about 30 minutes away from San Diego, California and am blessed to say only about 20 minutes away from the beach!

Something that really defines me is my desire to challenge many sides of myself in the hopes of developing into a well-rounded individual. Whether it be challenging my creative side by writing (for both school and on the personal blog that I recently started), or by challenging my quantitative side by participating as a research assistant in the Duke Environmental Justice lab leveraging data to reveal environmental injustices, I love to bolster as many facets of myself as possible.

Nina Cervantes hitting a volleyball

Nina Cervantes playing volleyball at the beach.

This desire led me to working a Marketing Communications internship this past summer at RTI International, the original research institute in North Carolina’s Research Triangle Park. During the course of my internship, part of my role was communicating sometimes dense research into digestible content marketing pieces for potential clients and the general public that might be perusing RTI’s work. This included connecting with subject matter experts who had actually conducted the studies and working through the material to be able to understand it well enough to communicate its power and potential to the world. This aspect of my internship was definitely one of the most rewarding parts, so blogging for Duke Research seemed like the perfect opportunity for me to transfer the skills I learned at RTI International, while also continuing to build my communication and analytical skills.

In addition to writing for the Duke Research Blog, I am also heavily involved with the Duke Women’s Basketball program and this is the 3rd year I have participated as a student manager. Like I said previously, I also started working on the research team for Duke’s Environmental Justice Lab last year and am very excited to start seeing some results of my first research experience at Duke!

Looking forward, I’m excited to have the opportunity to meet some really influential leaders in the research world, connect to the power and potential of their research, and then share it with you all in the best way I can. To me, there are few things more rewarding than sharing the power of a new discovery!

Post by Nina Cervantes

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