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What does it take to be a successful PhD student? Two grad students in statistics weigh in

With so many different career options in life, how do you know that you’ve found the right one for you?

Graduate students Edric Tam and Andrew McCormack, when asked what they hope to be doing in ten years, said they’d choose to do exactly the kind of work they’re doing right now – so clearly, they’ve found the right path. Tam, who obtained his undergraduate degree in Biomedical Engineering, Neuroscience and Applied Mathematics from Johns Hopkins University, and McCormack, who came from the University of Toronto with a degree in Statistics, are now 5th-year PhD students in the Department of Statistical Sciences. Tam works with Professor David Dunson, while McCormack works with Professor Peter Hoff, and both hope to pursue research careers in statistics.

Edric Tam
Andrew McCormack

Research interests

For the past five years, McCormack has been doing more theoretical research, looking at how geometry can lend insights into statistical models. The example he gives is of the Fisher information matrix, a statistical model that many undergraduate statistics majors learn in their third or fourth year.

Tam, meanwhile, looks at data with unique graphical and connectivity structures that aren’t quite linear or easily modeled, such as a brain connectome or a social network. In doing so, he works on answering two questions – how can you model data like this, and how can you leverage the unique structure of the data in the process?

What both Tam and McCormack like about the field of statistics is that, as Tam puts it, “you get to play in everyone’s backyard.” Moreover, as McCormack says, the beauty of theoretical research is that, while it’s certainly more time-consuming and incremental, it is often timeless, giving insight into something previously unknown.

On walking the research path

What does it take to be a successful PhD student? Both McCormack and Tam agree that a PhD is just a degree – anyone can get one if you work hard. But what sustains both through a career in research is a passion for what they do. Tam says that “you need inherent motivation, curiosity, passion, and drive.” McCormack adds that it helps if you work on problems that are interesting to you. 

Tam, who spent some time in a biomedical engineering lab during his undergraduate years, remembers reading about math and statistics the entire time he was there, which signaled to him that maybe, biomedical engineering wasn’t for him. McCormack’s defining moment occurred in the proof-based classes he took while as an undergraduate. He initially wanted to pursue a career in finance, but he quickly became enamored by the elegant precision of mathematical proofs – “even if all you’re proving is that 1+1=2!”

“You need inherent motivation, curiosity, passion, and drive.”

Edric tam, on what it takes to pursue a career in research

Even with passion for what you do, however, research can have its ups and downs. McCormack describes the rollercoaster of coming up with a new idea, convinced that “this is a paper right here”, and then a day later, after he’s had time to think about the idea, realizing that it isn’t quite up to the mark. Tam, who considers himself a pretty laidback person, sometimes finds the Type A personalities in research, as in any career field, too intense. Both McCormack and Tam prefer to not take themselves too seriously, and both exude a love for – and a trust of – the process.

Tam, not taking himself too seriously

Reflections on the past and the future

Upon graduation, McCormack will move to Germany to pursue a post-doc before beginning a job as Assistant Professor in Statistics at the University of Alberta. Tam will continue his research at Duke before applying to post-doc programs. In reflecting on their paths that have brought them till now, both feel content with the journey they’ve taken.

Tam sees the future in front of him – from PhD to post-doc to professorship – as “just a change in the title, with more responsibility”, and is excited to embark on his post-doc, where he gets to continue to do the research he loves. “It doesn’t get much better than this,” he laughs, and McCormack agrees. When McCormack joins the faculty at the University of Alberta, he’s looking forward to mentoring students in a much larger capacity, although he comments that the job will probably be challenging and he’s expecting to feel a little bit of imposter syndrome as he settles in.

When asked for parting thoughts, both Tam and McCormack emphasize that the best time to get into statistics and machine learning is right now. The advent of ChatGPT, for example, could replace jobs and transform education. But given their love for the field, this recommendation isn’t surprising. As Tam succinctly puts it, “given a choice between doing math and going out with friends, I would do math –  unless that friend is Andy!”

Post by Meghna Datta, Class of 2023

Senior Jenny Huang on her Love for Statistics and the Scientific Endeavor

Statistics and computer science double major Jenny Huang (T’23) started Duke as many of us do – vaguely pre-med, undecided on a major – but she knew she had an interest in scientific research. Four years later, with a Quad Fellowship and an acceptance to MIT for her doctoral studies, she reflects on how research shaped her time at Duke, and how she hopes to impact research.

Jenny Huang (T’23)

What is it about statistics? And what is it about research?

With experience in biology research during high school and during her first year at Duke, Huang toyed with the idea of an MD/PhD, but ultimately realized that she might be better off dropping the MD. “I enjoy figuring out how the world works” Huang says, and statistics provided a language to examine the probabilistic and often unintuitive nature of the world around us.

In another life, Huang remarked, she might have been a physics and philosophy double major, because physics offers the most fundamental understanding of how the world works, and philosophy is similar to scientific research: in both, “you pursue the truth through cyclic questioning and logic.” She’s also drawn to engineering, because it’s the process of dissecting things until you can “build them back up from first principles.”

At the International Society for Bayesian Analysis summer conference in Montreal

Huang’s research and the impact of COVID-19

For Huang, research started her first year at Duke, on a Data+ team, led by Professor Charles Nunn, studying the variation of parasite richness across primate species. To map out what types of parasites interacted with what type of monkeys, the team relied on predictors such as body mass, diet, and social activity, but in the process, they came up against an interesting phenomenon.

It appeared that the more studied a primate was, the more interactions it would have with parasites, simply because of the amount of information available on the primate. Due to geographic and experimental constraints, however, a large portion of the primate-parasite network remained understudied. This example of a concept in statistics known as sampling bias was muddling their results. One day, while making an offhand remark about the problem to one of her professors (Professor David Dunson), Huang ended up arranging a serendipitous research match. It turned out that Dunson had a statistical model that could be applied to the problem Nunn and the Data+ team were facing.

The applicability of statistics to a variety of different fields enamored Huang. When COVID-19 hit, it impacted all of us to some degree, but for Huang, it provided the perfect opportunity to apply mathematical models to a rapidly-changing pandemic. For the past two summers, through work with Dunson on a DOMath project, as well as Professor Jason Xu and Professor Rick Durrett, Huang has used mathematical modeling to assess changes in the spread of COVID-19.

On inclusivity in research

As of 2018, just 28% of graduates in mathematics and statistics at the doctoral level identified as women. Huang will eventually be included in this percentage, seeing as she begins her Ph.D. at MIT’s Department of Electrical Engineering and Computer Science in the fall, working with Professor Tamara Broderick.

“When I was younger, I always thought that successful and smart people in academia were white men,” Huang laughed. But that’s not true, she emphasizes: “it’s just that we don’t have other people in the story.” As one of the few female-presenting people in her research meetings, Huang has often felt pressure to underplay her more, “girly” traits to fit in. But interacting with intelligent, accomplished female-identifying academics in the field (including collaborations with Professor Cynthia Rudin) reaffirms to her that it’s important to be yourself: “there’s a place for everyone in research.”

At the Joint Statistical Meetings Conference in D.C with fellow researcher Gaurav Parikh

Advice for first-years and what the future holds

While she can’t predict where exactly she’ll end up, Huang is interested in taking a proactive role in shaping the impacts of artificial intelligence and machine learning on society. And as the divide between academia and industry is becoming more and more gray, years from now, she sees herself existing somewhere in that space.

Her advice for incoming Duke students and aspiring researchers is threefold. First, Huang emphasizes the importance of mentorship. Having kind and validating mentors throughout her time at Duke made difficult problems in statistics so much more approachable for her, and in research, “we need more of that type of person!”

Second, she says that “when I first approached studying math, my impatience often got in the way of learning.” Slowing down with the material and allowing herself the time to learn things thoroughly helped her improve her academic abilities.

Being around people who have this shared love and a deep commitment for their work is just the human endeavor at its best.

Jenny huang

Lastly, she stresses the importance of collaboration. Sometimes, Huang remarked,“research can feel isolating, when really it is very community-driven.” When faced with a tough problem, there is nothing more rewarding than figuring it out together with the help of peers and professors.  And she is routinely inspired by the people she does research with: “being around people who have this shared love and a deep commitment for their work is just the human endeavor at its best.”

Post by Meghna Datta, Class of 2023

(Editor’s note: This is Jenny’s second appearance on the blog. As a senior at NC School of Science and Math, she wrote a post about biochemist Meta Kuehn.)

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

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

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