From promoting Pi Day on live TV to penning articles in Scientific American, Max Springer (Ph.D. ’25, applied mathematics & statistics, and scientific computation) brings math to the masses. 

If you’ve ever wondered about artificial intelligence (AI) chatbot hallucinations, the accuracy of tornado science in the 1996 film “Twister” or whether the “stable marriage problem” in mathematics could yield better matches on the reality dating show “Love Island,” just ask University of Maryland recent graduate Max Springer (Ph.D. ’25, applied mathematics & statistics, and scientific computation).

Through a 2024 Mass Media Science & Engineering Fellowship with the American Association for the Advancement of Science, Springer wrote about mathematical topics and more for Scientific American, a publication he devoured for years but never dreamed of writing for. “I grew up reading Scientific American,” he said. “My grandpa got me a subscription when I was in middle school and I never really stopped.”

Springer doesn’t plan to pursue a career in journalism—instead, he will study fairness in algorithms as a postdoc at Princeton starting this September. However, a grounding in science communication helped Springer break down complex subjects and share his passion for math with a wider audience. Most recently, Springer joined Mathematics Professor Larry Washington for a Pi Day interview with Baltimore-based station WJZ-TV. The two mathematicians explained pi’s relevance beyond its connection to sweet treats, and in doing so, showed that math can be accessible—and even entertaining.  “People think that math is so abstract and hard to understand,” Springer said, “but when you write a good story about it in layman’s terms, readers quickly get drawn in.”

‘A beautiful merging’

Springer’s appreciation for popular science started at a young age. He recalls looking up to astronomer Carl Sagan and Bill Nye, the TV host who introduced a generation of children to the fun side of science, technology, engineering and mathematics. “Those were the first people who taught me about science and, above all else, to be curious about everything,” Springer said.

At times, Springer’s fascination with “everything” made it challenging for him to find his niche. During his sophomore year at Cornell University, a professor helped Springer pick a major as the declaration deadline loomed. “He didn't tell me to pursue math, but he emphasized that you can explain so many different phenomena using mathematical principles,” Springer said. “Studying math was a beautiful merging of all my interests.”

That advice ultimately sealed Springer’s decision to pursue a career in applied math. After graduating with his bachelor’s degree in mathematics in 2019, Springer completed a yearlong research position at Yale’s medical school, where he used machine learning and statistical methods to predict when patients with epilepsy might experience seizures. While looking for a graduate school that would continue to expand his diverse interests, Springer felt that UMD’s interdisciplinary applied mathematics & statistics, and scientific computation program would offer the best variety. 

The price of fairness 

After joining UMD in 2020, Springer started conducting research with Professor Mohammad Taghi Hajiaghayi, who holds the Jack and Rita G. Minker Professorship in UMD’s Department of Computer Science. Hajiaghayi introduced Springer to combinatorial problems in algorithm design, including how to efficiently and equitably divide resources among people. “That got me interested in the idea of what it means for an algorithm to be fair, and conversely, what does it mean for an algorithm to be biased,” Springer said. 

Springer started analyzing clustering problems, or ways of grouping similar data points and discovered that these technical considerations could have real-world impacts. This included cases where the data used to create clustering algorithms contained underlying biases. “There are a lot of instances where clustering algorithms are used to decide mortgage or which ads to deliver to people,” Springer explained. “What's been shown countless times in the past decade is that these algorithms can inherently pick up on people's gender or race and discriminate on those grounds.”

Springer’s dissertation focused on one central question: Does incorporating certain fairness constraints into an algorithm make its solution—or outputs—less accurate or less optimal? Ultimately, Springer found that “the price of fairness” was significantly lower than he expected. “What I showed across a bunch of problems is that when you incorporate these fairness considerations, you don't degrade your solution by much,” Springer said. “I hope that my research serves as a statement and shows that we should be considering these problems through this lens.”

While engaged in these technical problems, Springer also thought about how to communicate his work—and science more generally—to non-experts. When he first started writing for Scientific American last summer, he struggled to switch from an academic style of writing to popular science, which aims to break down complex subjects as clearly as possible.

“I wrote an article about satellite debris collecting in the ozone layer, and the physics editor marked up my entire draft and said, ‘You’re writing like an academic,’” Springer said. “I finally realized that if you’re talking about a research result, you want to walk the reader through that discovery and show them that science is a natural pursuit and they can understand it, too.”

Responsible AI

Another one of Springer’s interests is a subject that attracts widespread attention: the ethics of AI. “The AI space is moving so quickly, especially with large language models,” he said. “A lot of my more recent research was about how we can steer these models to ensure that AI systems don't develop to be biased in any way.” During his last year at UMD, Springer worked on data efficiency problems related to machine learning as an intern with Google Research’s Algorithms and Optimization Team. He also served on the inaugural student committee for the Association for the Advancement of Artificial Intelligence (AAAI), an international scientific society aimed at advancing safe and responsible AI that benefits society. 

“Graduate students are the backbone of AI research, especially in academia, so there are a number of things that we wanted to work on,” Springer said of his work with AAAI. “In particular, I wanted to work on mentorship and developing a host of resources for students who are interested in AI research.”

Springer helped to coordinate the AAAI’s first hackathon, in which competing teams developed “cutting-edge AI solutions” over a weeklong virtual event in February. Harking back to his interest in science communication, Springer also supported the development of an AAAI podcast series with student hosts interviewing AI experts. As Springer reflects on his time at UMD and starts packing his bags for Princeton, he feels that familiar sense of curiosity propelling him forward.

“When I read an article or a preprint that comes up, I’m instantly generating research questions,” Springer said. “I think that’s probably a good sign that I’m ready to start defining my own research. It’s super exciting.”

Written by Emily Nunez

AMSC Ph.D. student Shashank Sule (M.S. ’24, applied mathematics & statistics, and scientific computation) helps develop methods that could improve brain disease diagnostics and molecular simulations.

Shashank Sule (M.S. ’24, applied mathematics & statistics, and scientific computation) considers himself a latecomer to math. As an Amherst College freshman in 2016, he wanted to pursue chemistry research but wasn’t accepted into his preferred lab. 

Undeterred, he enrolled in a Fourier analysis math class to learn the basic tenets of Fourier transform infrared spectroscopy, an analysis technique used in chemistry, to bolster his skill set before reapplying. He didn’t expect this math class to change his plans completely. “I took that class and thought, ‘Okay, I don't want to major in chemistry anymore. I'd like to do mathematics instead,’” Sule recalled. “I liked how a lot of the arguments in Fourier analysis fit together elegantly and how far-reaching the applications of that subject were.”

Sule has been studying math ever since and is now a Ph.D. student in the University of Maryland’s applied mathematics & statistics, and scientific computation (AMSC) program. With applications in medical imaging and drug discovery, Sule’s research blends machine learning with mathematical techniques like applied harmonic analysis—a method that includes Fourier analysis and breaks functions down into smaller pieces for further study. Sule’s love of dissecting problems has aided his research, which aims to identify techniques for improving brain disease diagnostics and molecular simulations.

“I've always had a tendency to deconstruct or analyze things, and I also liked seeing how arguments fit together,” Sule said. “I think that is something that attracted me to math in the first place.”

Breaking it down

Though Sule pursued math later than some of his peers, a study abroad program in fall 2018 helped to bring him up to speed. As an undergraduate student, he spent a semester learning from Hungarian mathematicians in Budapest—a humbling and rewarding experience, according to Sule. “They’re famous for their problem-solving culture, so there were a lot of Olympiad-style problems,” Sule said. “I wasn’t used to that since I wasn't in the math world as a kid, but at some point, I started getting the hang of it and it became really fun.”

When Sule returned to Amherst, his advisor, University of Maryland alum Karamatou Yacoubou Djima (M.S. ’11, Ph.D. ’15, applied mathematics & statistics, and scientific computation) suggested that he focus on harmonic analysis for his undergraduate thesis. Sule was instantly captivated by the field’s applications in everything from medical imaging to machine learning—so much so that he decided to keep pursuing this research after earning his bachelor’s degree in mathematics in 2020. He chose UMD’s AMSC program because of his advisor’s recommendation and the Norbert Wiener Center for Harmonic Analysis. Since joining UMD, Sule has had the chance to explore both sides of his mathematical interests—pure and applied. “What gives me a lot of joy is finding good proofs but also finding mathematical arguments that lend themselves well to computation,” Sule said. “I think that's a really interesting side of harmonic analysis.”

Real-world research

In his current research, Sule works closely with his co-advisors, Mathematics Professors Wojciech Czaja and Maria Cameron. With Czaja, he uses a mix of classical and deep learning methods to develop better methods of identifying changes in myelin—a protective layer around nerve fibers and an important biomarker for brain disease. “If you want to diagnose Alzheimer's, traumatic brain injury or multiple sclerosis, there is this component in the brain called myelin, and researchers want to measure how much myelin each part of the brain has,” Sule said of their research, which was funded by the National Institutes of Health.
Specifically, their research approach aims to improve estimates of a patient’s myelin water fraction (MWF), which describes the amount of water within myelin and can indicate brain health. Their method would complement MRI, which is often imperfect at separating myelin from other features in the brain. Sule co-authored a paper showing that a machine learning technique called regularization can help tune out “noise” in the MRI data and improve the accuracy of MWF measurements. “This could serve as an additional diagnostic tool,” Sule said of the potential applications. “For instance, when a radiologist looks at a copy of a brain scan, they could have a tool on their screen that says the estimated myelin content. This could help them figure out what's going on in the brain.”

In separate research with Cameron, Sule develops new ways to speed up simulations of rare molecular events. Many processes that interest researchers—like how proteins fold or how chemicals react—happen too slowly to study on their actual timescales.  “It's like trying to see how a glacier melts by observing every centimeter of where the glacier is every week—you’re never going to see it in real life,” Sule said. “A lot of my work with Maria Cameron is about developing new mathematical techniques to understand these rare events.” While Sule focuses on getting the mechanics right, his research could one day advance the search for new and improved drugs to target a variety of human diseases. “One of our target goals is to find what's called the transition rate, which is connected to the rate of a chemical reaction,” Sule said. “It’s super relevant to drug design or studying chemical reactions.”

As Sule sharpens his research skills and reflects on everything he’s learned at UMD so far, he’s surprised by how far he’s come—as a mathematician and as a researcher working on real-world problems.
“Coming to graduate school, I never would have imagined the research I've worked on, especially the applications,” Sule said. “I very much still think of myself as a mathematician, but the exposure I've gotten to medical imaging, drug discovery and just knowing what things people outside of the math world care about has been really exciting and important to learn.”

Written by Emily Nunez

She graduated with a bachelor’s degree in mathematics and a bachelor’s degree in physics.

Jade LeSchack was selected as the student speaker for the University of Maryland’s College of Computer, Mathematical, and Natural Sciences 2025 Undergraduate Commencement Ceremony on May 22, 2025.. The ceremony will honor the college's August 2024, December 2024 and May 2025 graduates receiving bachelor's degrees.

Jade LeSchack is graduating with a bachelor’s degree in mathematics and a bachelor’s degree in physics in May 2025. She is a Design Cultures & Creativity student in the Honors College, and her capstone project, Black Creatives Matter, won an award for creativity in pursuit of anti-racist justice.

LeSchack conducted research in two quantum science groups at UMD. She worked in the Porto-Rolston ultracold atoms lab on experimental projects with electronics and lasers. She currently conducts research in Nicole Yunger Halpern’s quantum steampunk group, studying how thermodynamic laws and phenomena arise in quantum systems. She received a MathQuantum Fellowship from UMD’s Institute for Physical Science and Technology to conduct her research. Beyond UMD, LeSchack was an undergraduate research assistant at the University of Waterloo and studied abroad at the University of Zürich.

LeSchack is active in UMD’s quantum ecosystem and participates in and organizes quantum computing hackathons around the world. She also founded the Undergraduate Quantum Association in her first semester to connect students with UMD’s resources in quantum science and technology. She led numerous initiatives through the club, including the quantum track of the Bitcamp hackathon and an annual quantum career fair, which is now the Quantum Leap Career Nexus. She has been a Society of Physics student member, volunteering as a tutor and physics demonstrator, and a Startup Shell Fellow. She plays for the UMD Women’s Club Ultimate frisbee team and is a member of Omicron Delta Kappa National Leadership Honors Society.

After graduation, LeSchack will pursue her Ph.D. in quantum physics at the University of Southern California in the fall.