Senior mathematics and computer science double major Alex Yelovich’s passion for numbers helped him thrive in UMD’s Math and Robotics clubs. 

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When Alex Yelovich came to the University of Maryland as a freshman in 2021, he didn’t waste any time getting involved. Before classes started that fall, he emailed the UMD Math Club’s then-president to ask how to join.

“I guess I didn’t want to leave it to chance,” Yelovich joked. “I grew up loving math all the way through high school and was inspired by my calculus teacher to explore all things math, including the history of calculus and what you can do with numbers. I couldn’t picture myself at UMD without math, which is why I joined the club and chose to be a math major as soon as I could.”

Today, Yelovich is a senior mathematics and computer science double major and Math Club president. He plans the club’s activities year-round, from guest talks to game nights (including integration bees, a bracketed competition to solve complicated integrals, or math pictionary–a club favorite!). He also orchestrates bi-weekly presentations for UMD math students, staff and faculty members that showcase the diverse world of mathematical thinking at all levels. 

“The Math Club provides a platform for undergrads, graduate students and professors to present on any math-related topic of their choosing, just based on their interest or passion,” said Yelovich, who gave a talk on the history of the French mathematician François Viète and his impact on solving the cubic. “It’s a safe place where people can share their theories, ideas and projects with the rest of the department and have meaningful discussions about them beyond a classroom.”

Yelovich’s journey to leadership began almost by accident. During his first year at UMD, he enrolled in MATH 340: Multivariable Calculus, Linear Algebra and Differential Equations, an accelerated course designated for advanced incoming freshmen. Although it differed greatly from his high school math classes, Yelovich loved it. 

“In high school, students feel like they’re on this pathway that ends at calculus,” Yelovich explained. “But MATH 340 showed me everything you could do in math beyond that. My experience in the class ignited my excitement to explore an even wider variety of math content through my activities and courses.”

Impressed by Yelovich’s enthusiasm and aptitude, Mathematics Principal Lecturer Wiseley Wong—who is also the Math Club’s faculty advisor—approached him at the end of the semester to ask if Yelovich would be interested in becoming a Math Club officer. “Alex was always on top of things in the course, even asking what to read ahead for the next class,” Wong said, noting that he was impressed by Yelovich’s math skills and love of learning.

“Long story short, I got ‘promoted’ over the years, and now I’m giving back to the organization that’s been with me since the beginning and helping the next generation of math majors get involved,” Yelovich said.

Finding new ways to explore math 

Yelovich also shares his knowledge and leadership skills with UMD’s Robotics Club, where he serves as a technical lead for Qubo, one of three robots being developed by UMD student-led teams. Capable of navigating and course-correcting itself through underwater obstacles without direct remote guidance, Qubo is the club’s smallest (weighing almost 50 pounds) and longest-running robot project to date, starting development in 2014. 

“Qubo is our entry for RoboSub, this annual competition where you can earn points depending on how well your robot makes decisions on its own while submerged underwater,” Yelovich explained. “We’ve equipped Qubo with pressure sensors, thrusters, stereo vision cameras and even a custom-made torpedo launch system. I started working on Qubo my freshman year, became its software manager and now I’m its project manager. All the technical decisions we make as a team go through me before they’re made on the robot and I do my best to manage Qubo’s progress.”

Yelovich began managing Qubo’s software his junior year, the same year he became the president of the Math Club. Balancing his two leadership roles was difficult but immensely rewarding, connecting his love for math with his passion for computer science and developing software. Two summers ago, Yelovich interned at Lockheed Martin, where he worked to improve the computing power of a central computer system node that he likened to a “basic four-function calculator.” 

“There’s a lot you can do to connect math with problem-solving in the real world. For example, we use linear algebra for computer vision, allowing robots to interpret visual data from images taken with their camera to orient themselves,” Yelovich said. “I developed some creative thinking skills during my internship by combining the concepts of automatic differentiation with operator overloading, which essentially tells the computer the rules of differentiation for various expression, such as the chain rule or well-known derivatives of elementary functions, enabling the computer to compute both the functional value and its derivative at a point. They’re all connections between math and computer science that aren’t so obvious.”

After he graduates, Yelovich plans to move to Atlanta, where he landed a job as a firmware engineer at Mueller Systems, the oldest manufacturer of water meters in the U.S. Looking back, Yelovich feels grateful for the skills he developed at UMD and hopes that students just beginning their journeys at UMD will make the most of the opportunities available to them on campus, just as he did.

“Take advantage of the excellent professors in the math department,” he said. “Go to office hours. Even if you’re already comfortable with the material, get to know your professors and have conversations and grow from them. You never know where these moments will take you.”

Written by Georgia Jiang

Mathematics major Jerome Mathew wants to make a low-profile yet vital profession more accessible to students.

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When Jerome Mathew first encountered the concept of actuarial science during his freshman year at the University of Maryland, he was drawn to its unique blend of mathematical rigor and business application 

“It’s a combination of math, business, economics and more,” said Mathew, now a senior double majoring in mathematics and computer science. “The Actuary field is really entrenched in the fabric of our society, even though most people outside of the field don’t really know what the job of an actuary entails.”

Actuarial science, with roots that date back thousands of years to the Code of Hammurabi in ancient Babylon and insurance-like programs in ancient Greece and Rome, is the art of calculating and quantifying risk and compensation for losses. In the late 17th and 18th centuries, English mathematicians John Graunt and Edmond Halley (best known for his namesake comet) pioneered the idea of analyzing longevity and death in segmented population groups, forming the basis of modern life insurance—and the start of the actuarial profession.

Today, actuaries are essential across numerous industries, from insurance and health care to banking and government. Modern actuaries manage risk, specifically employing sophisticated  mathematical models to predict outcomes ranging from natural disaster impacts to health care costs. That mathematical connection sparked Mathew’s interest.

“Actuarial math appealed to me because it has such broad implications, but there wasn’t much awareness of the profession on campus,” Mathew said. “I needed to learn more, so I joined the Actuary Club on campus that same semester and became its third member.” 

UMD’s Department of Mathematics does not currently offer an actuarial sciences degree, although students can take classes within the statistics major track that heavily overlap with actuarial exam requirements and receive actuarial academic advising. Non-math majors can also enroll in the department’s actuarial mathematics minor, which provides them with the mathematical foundation needed for actuarial work. This academic structure made the Actuarial Club even more important for math students interested in actuarial careers like Mathew, as it aimed to bridge the gap between classroom theory and professional practice. 

But Mathew soon ran into a problem: the club was small and its future was in jeopardy after he joined. “Once the then-president and vice president graduated, I became the de facto president and sole member,” Mathew said. “For a while, I was lost and didn’t know what to do to keep the Actuary Club alive. It was just me and our faculty advisor, Math Professor Eric Slud, who offered me a lot of valuable perspective because he’s a credentialed actuary himself.” From a one-person club to a thriving community of aspiring actuaries

To come up with a plan to revive the club and learn more about the skills required of actuary professionals, Mathew enrolled in STAT 470: Actuarial Mathematics. Originally designed decades ago by Slud to introduce students to the mathematical foundations of risk assessment, the course proved transformative because it connected Mathew with other students interested in actuarial careers.  

Drawing on relationships formed in the class, Mathew overhauled the original club and created a new leadership team, website and social media presence with his classmates. “I realized I couldn’t do it all alone. The best ideas really come when you have a team and everyone works together to put those ideas to action,” Mathew said. “With everyone’s help, we brainstormed ideas about how to reach out to people.”

The revitalized Actuarial Club adopted a new approach to membership. Instead of targeting only students who were already committed to becoming actuaries, they focused on increasing exposure to those who were curious about or simply unaware of the field as a potential career option. The strategy worked. The club quickly expanded to an executive board with four positions and a growing membership roster. Now, the group generally meets twice a semester with guest speakers who provide insight into the actuarial profession and advice to students interested in pursuing the career. “A recent presentation from an industry professional drew about 25 attendees to our club meeting,” Mathew noted. “We managed to get the word out and people wanted to learn more.” Slud, who has been the go-to UMD faculty member for actuarial sciences expertise since the 1990s, was impressed by the group’s growth and development under Mathew’s leadership.

“In my experience, it seemed like there were never more than about a dozen students university-wide who were planning an actuarial career and taking actuarial exams, but Jerome has been very entrepreneurial in encouraging continuing participation beyond the club’s previous format,” Slud noted.  

In the future, Mathew hopes to incorporate exam study sessions to help students prepare for the rigorous professional exams required to become a credentialed actuary and other tests that keep actuaries current with industry changes and help with career advancement in the field.“The profession requires you to be very well-trained and up-to-date, which can be intimidating,” Mathew explained. “Having the credentials sets the stage for getting a job in the field and being successful at it.”

To advance his own practical experience, Mathew recently completed an actuarial internship at Cigna Group and will soon work in health care consulting at Aon, a multinational firm that offers a variety of risk-mitigation products, this summer. As Mathew approaches the end of his tenure as Actuary Club president, he hopes to pass on his passion to a new generation of aspiring actuaries and keep the club alive. “If you’re curious about what an actuary does and if becoming one is possibly the right fit for you, you should definitely come at least once—if only to understand what the field is,” Mathew said. “Being a math major with a solid baseline of quantitative skill is great, but computer science, econ and finance majors have a lot to contribute, too. We welcome anyone who wants to learn a different way of applying their math skills.”

Written by Georgia Jiang

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. 

At Google Cloud, Valerie Hyde (M.S. ’05, Ph.D. ’07, applied mathematics & statistics, and scientific computation) taps into cutting-edge technologies to take business enterprises into the future. 

Whether it’s making sure a virtual agent gets a customer’s drive-thru burger order right or helping an online shopper find that flowered dress they’re looking for, Valerie Hyde (M.S. ’05, Ph.D. ’07, applied mathematics & statistics, and scientific computation) is all about problem-solving for businesses—and doing it with next-generation technologies like artificial intelligence (AI).  

For the past five years, as head of the North American Artificial Intelligence Customer Engineering team at Google Cloud, Hyde has tapped into the power of technologies like AI and machine learning to tackle big and small challenges for businesses, drawing from her 20 years of experience at companies including Microsoft, SAS and IBM. 

It’s not where she thought statistics would take her 20 years ago—it’s better. 

“I never could have imagined this. Sometimes I can't believe that I get to do what I'm doing at this time in history at such an innovative company,” Hyde reflected. “For so many years, it seemed like only a small group of people really cared about statistics and data science, but now everybody is doing AI, and it's really people interacting with technology.” 

From math to statistics 

For as long as Hyde can remember, math and science were always a part of life. 

   “I was always surrounded by it—both of my parents were professors at state schools in New York—my dad was a chemistry professor, and my mom was a computer science professor,” Hyde explained. “I always liked math the most in school, even more than science, because there were problems that I could solve definitively. That gave me a lot of confidence in my ability to solve problems and think logically.” 

As an undergraduate majoring in mathematics and economics at Binghamton University in New York, Hyde discovered that the math classes she liked best focused on statistics. By the time she earned her degree in 1998, she knew that applying math to solve real-world business problems was something she wanted to do. 

She landed a job with the antitrust group at the U.S. Department of Justice, where she found herself working on a high-profile case that was getting plenty of attention. 

“I used math and programming to help the government quantify its antitrust cases,” she recalled. “The case that people may remember was when the Justice Department sued Microsoft over packaging its Internet Explorer browser with its Windows operating system.” 

Though Hyde was working full time, she was still thinking about graduate school—and getting some gentle encouragement from her father. 

“Pretty much every call with my dad was, ‘When are you going to go back to school? When are you going to get a higher degree?’  I felt like that was what I was supposed to do,” Hyde recalled. “Both my parents are Ph.D.s, and I think my dad envisioned that I would do that as well. He wanted to make sure that I was always in a stable position, which is possible with advanced degrees in math and science.” 

In 2002, Hyde started graduate school at the University of Maryland, not coincidentally, where her father earned his Ph.D. in chemistry in 1969. She soon connected with Mathematics Professor Benjamin Kedem, who quickly became one of her favorite professors. 

“Professor Kedem was a mainstay in the Statistics Department. I remember he admitted me into the program because I had an econ background and had solved applied math problems. Another favorite professor was Paul Smith, who taught linear models, which I really enjoyed—it was more applied, doing linear algebra to calculate model parameters,” Hyde said. “I took an independent study with him where we examined advanced statistical and machine learning problems. At the time, using computer vision models to detect handwritten text and numbers was newer; it wasn’t an easily solved problem like it is now.” 

Summer internships along the way took Hyde to the U.S. Census Bureau and AT&T. 

“It was really neat working at AT&T Research; there were so many smart researchers doing interesting work there,” Hyde recalled. “The project that I remember the most was research on speech-to-text. Even though I didn’t work on that project, I was one of the sample voices used to train their models.” 

Challenge after challenge, Hyde worked her way forward. 

“I think the biggest thing that I got out of grad school was tenacity. The work was hard for me, and I learned to take it on in small pieces and build accomplishments,” Hyde explained. “I was part of a study group, and we spent the first few years together in one of the rooms on the third floor of the science and engineering library. We each had different strengths and worked together to help get our group through. Sometimes I think I never would have made it through without them and I hope they would say the same thing about me.”  

Hyde earned her master’s degree in 2005 along the way to her Ph.D., eventually publishing three papers focusing on price curves, bidding distribution and other aspects of eBay auctions, which were highly popular at the time. 

Smarter solutions 

After earning her doctorate in 2007, Hyde spent the next several years putting her statistics, modeling and data science skill set to work in various roles, working in marketing analytics at Accenture, and technical sales building proof of concepts at SAS, IBM and Microsoft before joining Google Cloud in 2020. There, she leads a team of customer engineers at Google Cloud, helping clients incorporate technologies like AI and machine learning into smarter solutions to take their businesses into the future. 

“We help our customers understand the technology, incorporate it into their business, and troubleshoot as they implement the solution.  Hyde said. “I like working with customers to help them solve a problem in a way that they might not have thought about.” 

Many of these business clients find that their customers notice this next-generation problem-solving, like adding AI-powered virtual assistants at fast food restaurants that can understand exactly what someone is ordering. 

“That used to be very difficult to solve because it would have been speech-to-text models to intents and then a huge decision-tree like conversational architectures to try to understand what somebody wanted—and it was hit or miss whether it would get it right,” Hyde said. “But now, with large language models, you don't have to do that— the models are able to understand different versions of how you would order your burger.” 

For retail businesses whose customers are looking for specific products, AI takes searches beyond keywords to semantic search or even visual search to help people find exactly what they’re looking for. 

“In just a few years, search results have improved dramatically with the improvement of multi-model models. You can upload a picture with a flower pattern you like and say with text or voice that you want that on a dress, and AI will understand that you’re searching for a dress that has flowers on it.” Hyde said. “And the thing is, consumers are starting to expect all of these things.” 

A strong foundation 

Though the technology she works with every day is beyond anything she imagined 20 years ago, Hyde still relies on the valuable math and statistics skills she developed at UMD. 

“At Maryland, I learned to understand how to solve problems rather than just memorize how to solve problems,” Hyde explained. “I now have the ability to understand and explain what is going on with a speech-to-text model or a computer vision model or a large language model because I have the underlying knowledge of statistics, modeling and the assumptions that you have to make to build the models. UMD really gave me a great foundation.”  

For Hyde, it’s all about problem-solving on the cutting edge of technology. And she wouldn’t want to be doing anything else. 

“The technology is changing so rapidly that it almost feels old shortly after being introduced because research in AI is flourishing,” she said. “Google is an innovative company with a strong research arm, so they're always at the forefront of what's going on, giving us new ways to solve problems. That’s really exciting.”  

Written by Leslie Miller