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How Research Helped One Pre-med Discover a Love for Statistics and Computer Science

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If you’re a doe-eyed first-year at Duke who wants to eventually become a doctor, chances are you are currently, or will soon, take part in a pre-med rite of passage: finding a lab to research in.

Most pre-meds find themselves researching in the fields of biology, chemistry, or neuroscience, with many hoping to make research a part of their future careers as clinicians. Undergraduate student and San Diego native Eden Deng (T’23) also found herself plodding a similar path in a neuroimaging lab her freshman year.

Eden Deng T’23

At the time, she was a prospective neuroscience major on the pre-med track. But as she soon realized, neuroimaging is done through fMRI. And to analyze fMRI data, you need to be able to conduct data analysis.

This initial research experience at Duke in the Martucci Lab, which looks at chronic pain and the role of the central nervous system, sparked a realization for Deng. “Ninety percent of my time was spent thinking about computational and statistical problems,” she explained to me. Analysis was new to her, and as she found herself struggling with it, she thought to herself, “why don’t I spend more time getting better at that academically?”

Deng at the Martucci Lab

This desire to get better at research led Deng to pursue a major in Statistics with a secondary in Computer Science, while still on the pre-med track. Many people might instantly think about how hard it must be to fit in so much challenging coursework that has virtually no overlap. And as Deng confirmed, her academic path not been without challenges.

For one, she’s never really liked math, so she was wary of getting into computation. Additionally, considering that most Statistics and Computer Science students want to pursue jobs in the technology industry, it’s been hard for her to connect with like-minded people who are equally familiar with computers and the human body.

“I never felt like I excelled in my classes,” Deng said. “And that was never my intention.” Deng had to quickly get used to facing what she didn’t know head-on. But as she kept her head down, put in the work, and trusted that eventually she would figure things out, the merits of her unconventional academic path started to become more apparent.

Research at the intersection of data and health

Last summer, Deng landed a summer research experience at Mount Sinai, where she looked at patient-level cancer data. Utilizing her knowledge in both biology and data analytics, she worked on a computational screener that scientists and biologists could use to measure gene expression in diseased versus normal cells. This will ultimately aid efforts in narrowing down the best genes to target in drug development. Deng will be back at Mount Sinai full-time after graduation, to continue her research before applying to medical school.

Deng presenting on her research at Mount Sinai

But in her own words, Deng’s most favorite research experience has been her senior thesis through Duke’s Department of Biostatistics and Bioinformatics. Last year, she reached out to Dr. Xiaofei Wang, who is part of a team conducting a randomized controlled trial to compare the merits of two different lung tumor treatments.

Generally, when faced with lung disease, the conservative approach is to remove the whole lobe. But that can pose challenges to the quality of life of people who are older, with more comorbidities. Recently, there has been a push to focus on removing smaller sections of lung tissue instead. Deng’s thesis looks at patient surgical data over the past 15 years, showing that patient survival rates have improved as more of these segmentectomies – or smaller sections of tissue removal – have become more frequent in select groups of patients.

“I really enjoy working on it every week,” Deng says about her thesis, “which is not something I can usually say about most of the work I do!” According to Deng, a lot of research – hers included – is derived from researchers mulling over what they think would be interesting to look at in a silo, without considering what problems might be most useful for society at large. What’s valuable for Deng about her thesis work is that she’s gotten to work closely with not just statisticians but thoracic surgeons. “Originally my thesis was going to go in a different direction,” she said, but upon consulting with surgeons who directly impacted the data she was using – and would be directly impacted by her results – she changed her research question. 

The merits of an interdisciplinary academic path

Deng’s unique path makes her the perfect person to ask: is pursuing seemingly disparate interests, like being a Statistics and Computer Science double-major on the pre-med, track worth it? And judging by Deng’s insights, the answer is a resounding yes.

At Duke, she says, “I’ve been challenged by many things that I wouldn’t have expected to be able to do myself” – like dealing with the catch-up work of switching majors and pursuing independent research. But over time she’s learned that even if something seems daunting in the moment, if you apply yourself, most, if not all things, can be accomplished. And she’s grateful for the confidence that she’s acquired through pursuing her unique path.

Moreover, as Deng reflects on where she sees herself – and the field of healthcare – a few years from now, she muses that for the first time in the history of healthcare, a third-party player is joining the mix – technology.

While her initial motivation to pursue statistics and computer science was to aid her in research, “I’ve now seen how its beneficial for my long-term goals of going to med school and becoming a physician.” As healthcare evolves and the introduction of algorithms, AI and other technological advancements widens the gap between traditional and contemporary medicine, Deng hopes to deconstruct it all and make healthcare technology more accessible to patients and providers.

“At the end of the day, it’s data that doctors are communicating to patients,” Deng says. So she’s grateful to have gained experience interpreting and modeling data at Duke through her academic coursework.

And as the Statistics major particularly has taught her, complexity is not always a good thing – sometimes, the simpler you can make something, the better. “Some research doesn’t always do this,” she says – she’s encountered her fair share of research that feels performative, prioritizing complexity to appear more intellectual. But by continually asking herself whether her research is explainable and applicable, she hopes to let those two questions be the North Stars that guide her future research endeavors.

At the end of the day, it’s data that doctors are communicating to patients.

Eden Deng

When asked what advice she has for first-years, Deng said that it’s important “to not let your inexperience or perceived lack of knowledge prevent you from diving into what interests you.” Even as a first-year undergrad, know that you can contribute to academia and the world of research.

And for those who might be interested in pursuing an academic path like Deng, there’s some good news. After Deng talked to the Statistics department about the lack of pre-health representation that existed, the Statistics department now has a pre-health listserv that you can join for updates and opportunities pertaining specifically to pre-med Stats majors. And Deng emphasizes that the Stats-CS-pre-med group at Duke is growing. She’s noticed quite a few underclassmen in the Statistics and Computer Science departments who vocalize an interest in medical school.

So if you also want to hone your ability to communicate research that you care about – whether you’re pre-med or not – feel free to jump right into the world of data analysis. As Deng concludes, “everyone has something to say that’s important.”

Post by Meghna Datta, Class of 2023

Undergraduate Researchers William He and Annie Wang Dig Deeper into Hypergraphs

Like most things during the height of the pandemic, research that could be conducted virtually was conducted virtually. And that’s why, although juniors William He and Annie Wang have been working together on a research project since last September, they’ve never actually met in person.

He, a Math major from Houston, and Wang, a Computer Science and Math double-major from Raleigh, both work in the lab of Professor Debmalya Panigrahi, where the focus is on research in theoretical computer science, particularly graph algorithms. Wang and He did work on hypergraphs, and, after I asked them to explain what hypergraphs were in the most elementary terms (I am not a Math major), they went back and forth on how exactly to relay hypergraphs to a lay audience.

Annie Wang

Here is what they landed on: hypergraphs are essentially generalizations of normal graphs. In a normal graph, there are edges –each edge connects two points. There are also vertices – each point is a vertex. But in a hypergraph, each edge connects multiple points.

He and Wang were looking at a generalization of graph reliability – if all edges disconnect at a certain probability, what is the probability that the graph itself will break down because crucial edges are disconnecting?

William He

Their research adds to existing research on maximum flow problems, which Wikipedia tells us “entail finding a feasible flow through a flow network to obtain the maximum possible flow rate.” In a landmark paper written by T.E. Harris and F.S. Ross in 1955, the two researchers formulated the maximum flow problem using an example of the Soviet railroad and considering what cuts in the railroad would disconnect the nation entirely – and what cuts could be made with little impact to railway traffic flow. 

Maximum flow problems are a core tenet of optimization theory, used widely in disciplines from math to computer science to engineering. You may not know what mathematical optimization is, but you’ve seen it in action before: in electronic circuitry, in economics, or unsurprisingly, used by civil engineers in traffic management.

It’s expected to be incredibly difficult to exactly calculate the target value of He and Wang’s question. They landed on an approximation that they know is far from the exact calculation, but still brings them closer to understanding hypergraph connectivity more fully.

The process

So what draws them to research? For He, it’s like an itch. He describes that “sometimes I’ll be watching a movie, and then thirty minutes in I’m thinking about a possible solution to a math problem and then I can’t focus on the movie anymore.” You can’t get on with things until you scratch the itch, but the best part to him is when things finally start to make sense. For Wang, research is just plain fun. She enjoys learning about algorithms and theorems, and she loves the opportunity to work with professors who are at the forefront of their field.

After Duke, He wants to pursue a PhD, likely in theoretical computer science, while Wang is still weighing her options – whether she wants to go into academia or industry. While He came into Duke as a prospective Economics major, in quarantine especially he realized just how much he enjoyed math for the sake of itself.

Wang, similarly, thought she would want to pursue software engineering, but she’s slowly realizing that she likes “solving the problems within the field – problems that I need a PhD to solve.” The magic of research, for her, is that “you’re solving problems that no one has answers to yet.” And wherever the future takes both of them, she says that in doing research, even at the undergraduate level, “you feel like you’re pushing the boundary a tiny bit, and that’s a cool feeling.”

Post by Meghna Datta, Class of 2023

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