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

For Ken Weiner (M.A. ’69, Ph.D. ’75, mathematics), a knack for math and a passion for teaching led to a 37-year career as a mathematics professor and a game-changing nonprofit that helps at-risk students succeed.

Ken Weiner (M.A. ’69, Ph.D. ’75, mathematics) was always on a path to math—maybe before he even realized it. “It’s a weird thing to say because I was always very good at numbers and I always really liked math, but I honestly never remember making a conscious decision that majoring in math was something I wanted to do,” Weiner reflected. “I swear, I think I kind of woke up one day and I already had 16 credits in math or something like that, and I said, ‘Geez, I guess I'm a math major!’”

Weiner knows now that math was the right path for him all along. From his days as an undergraduate at Brooklyn College to his years in graduate school at the University of Maryland, Weiner charted a course to a long, successful career—as a professor and academic leader who taught math at Montgomery College for 37 years and also as co- founder and president of the board of directors for Future Link. This game-changing Rockville, Maryland, nonprofit provides at-risk young adults with free self-advocacy training, mentoring, paid internships, and career counseling programs to help them reach their academic and career goals.
“We are serving first-generation-to-college students who face economic adversities, but most also have other adversities in their lives,” Weiner explained. “And the mission has always been that these young adults have the potential to succeed, but they just do not have the resources or adults around them who could provide them with the advice and support needed to prepare them for higher education and professional careers.”
For Weiner, now retired, making an impact as a teacher and mentor means everything.

“I was thrilled with my career. I loved teaching. I loved everything about it,” Weiner said.
“To me, the thing that rules the world are relationships, and so throughout my teaching career, a big part for me has been to try not only to teach, but to build relationships with students and make a difference.”

Figuring it out

As a kid growing up in Brooklyn, New York, Weiner took to math at an early age. “I remember when I was a young kid, my aunts and uncles would come over and they would give me all of these problems, they’d ask me, ‘What’s 28 times 32,’ and I’d have to figure it out,” Weiner recalled. “I started to really like math, and I had a cousin who was a math teacher who ultimately became sort of my mentor.” By the time Weiner finished his undergraduate degree in mathematics and started graduate school at UMD, he thought he was headed toward a career as an applied statistician. But when he started working as a teaching assistant and connected with an inspirational professor in the UMD Department of Mathematics, his perspective began to change.

“I started working with a professor named David Lay, who had been in the department for a number of years, and he was such a sensational teacher,” Weiner said. “I TAed under him for about three years, I loved all his mentorship and it had a huge impact on me. I think it really was one of the things that propelled me to move forward with teaching. ”After more than four years as a TA, Weiner realized he wanted to become the kind of mentor and teacher he saw in Lay. So, a few years after Weiner started his Ph.D., he found himself teaching mathematics at Montgomery College, eventually becoming a full-time professor after he earned his doctorate.
“I just loved everything about the environment and the whole experience. I got so into where I was at Montgomery, it was a great time to be there,” Weiner said. “And before I knew it, I just was sort of part of that culture and happy to be there.”

A new mission

Weiner continued teaching at Montgomery College until he retired in 2008. Then, he worked as a consultant on various college projects, including a task force charged with improving the college’s developmental math program and another project working with local nonprofits that set the stage for his next mission. “United Way, the City of Gaithersburg and the City of Rockville joined together, and they asked the college if anybody could teach all the nonprofits in Montgomery County how to measure outcomes, and so I, together with two colleagues, started doing that,” Weiner explained. “We must have trained 300 nonprofits in just a few years.”
As a result of that work, Weiner joined the board of directors at a transitional homeless shelter, and helped the nonprofit develop a new educational program for at-risk young adults. “We wanted to create a program that would help vulnerable young people avoid chemical addiction and homelessness, which is what that shelter was dealing with,” Weiner recalled. “Kids would age out of foster care at 18 and they would get thrown back into society with no preparation and no ability to navigate the real world and we wanted to help them.”

After some research and a lot of brainstorming, Future Link was born. What started as a small weekly seminar to help kids who needed mentoring and guidance has since grown into a full-on educational and career support program, with individualized services from paid internships, career coaching, tutoring and mentoring to scholarships, training and more—a place, as the program’s motto notes, ‘Where potential meets opportunity.’ For 17 years, Weiner has contributed his time and expertise as a leader, mentor and teacher in the program, and he couldn’t be prouder of what Future Link has accomplished.
“If you look at the program now, 17 years later, we have some 270 adult volunteers, including 150 adults who are doing one-on-one mentoring with students. We've recently created a career coaching program, and there are about 80 volunteers who work individually with each student about to leave college and go into the workplace to prepare them to get a job,” Weiner explained. “Future Link is a long-term commitment to every student that participates in the program to get them through the education they need for the career they're interested in.” For Weiner, who knows from his own experience just how valuable teaching and mentoring can be, making that kind of impact couldn’t be more important. And even now that he’s retired, students still reach out to say thank you.

“One of my former Future Link students is now a policeman in Baltimore. This is a kid that was struggling in El Salvador when he was young and he's now married with two young children and loves what he does,” Weiner explained, “Probably three times a week I get a text from him saying, “I don't know where I would be if it wasn't for what you did for me.” The feeling that you have impacted somebody's life that way, there's just nothing better.”

Written by Leslie Miller

New Brin Endowed Professors Uri Bader and Ron Peled bring a wealth of experience and research to the department.

New Brin Endowed Professors Uri Bader and Ron Peled bring a wealth of experience and research to the UMD Department of Mathematics.

Two internationally recognized mathematicians joined the University of Maryland in fall 2024. Uri Bader and Ron Peled—who hold Brin Endowed Professorships in Mathematics—bring significant research experience to the department, according to  Mathematics Chair Doron Levy.

“Ron Peled is an international leader in the fields of probability, mathematical physics and statistics mechanics. Hiring Professor Peled brought to the department one of the best probabilists in the world,” Levy noted. “Uri Bader is an extraordinary mathematician who works in an area of mathematics related to geometry, number theory, group theory, dynamical systems and functional analysis. This is a remarkably broad area of expertise for a mathematician. Professors Peled and Bader will both provide exceptional research opportunities to our students.”

The new Brin Endowed Professorships in Mathematics were established with a generous gift from UMD Mathematics Professor Emeritus Michael Brin and his wife Eugenia for $2 million, which was fully matched by the Maryland Department of Commerce. The match was made through the Maryland E-Nnovation Initiative (MEI), a state program created to spur basic and applied research in scientific and technical fields at colleges and universities. 

With these new endowed professorships, UMD’s Department of Mathematics gained a significant edge in attracting top mathematicians to its next-level mathematics programs.

“Hiring Uri Bader and Ron Peled required Maryland to compete with many top math departments,” Levy said. “The newly established Brin Professorships were instrumental in our ability to attract both of them to Maryland.”

Uri Bader: Finding new territories in mathematics

Bader’s mathematical research is all about exploring connections.

“I like seeing the connections between mathematical objects. Sometimes people describe mathematics as different lands—there is algebra, there is geometry, there is number theory, but I see it as one continent, with no clear borders in between,” Bader explained. “I’m hoping to be an explorer—I’m looking to find new territories in mathematics to explore and this is what keeps me going.”

Known as one of the deepest and most active experts in the area of mathematics that comes from the Furstenberg/Margulis school of ergodic theory and discrete subgroups of Lie groups, Bader studies geometric group theory, dynamical systems, operator algebras, complex geometry and more.

“I’m working at the crossroads of geometry, algebra and number theory and I’m studying group theory, which is the study of symmetry,” Bader said. “Group theory by itself is an interplay of two theories—geometry and algebra. Geometry because you have these geometric objects that you study symmetries of and algebra because you describe symmetries by algebraic means. In my research, I apply these tools to describe number theory.”

Bader grew up in Israel, earning his undergraduate degree and Ph.D. in mathematics from the Technion-Israel Institute of Technology and later becoming an L.E. Dickson Instructor at the University of Chicago and a lecturer and professor of mathematics at the Technion before joining the Weizmann Institute in 2015 as a professor of mathematics.  

For Bader, his position at UMD opens up a host of new opportunities.

“I have some fantastic colleagues here at UMD and I’m looking forward to collaborations with them, students and visitors,” Bader said. “The wonderful Brin Mathematics Research Center brings many top-notch researchers to UMD and offers an opportunity to invite other scholars to study mathematics together. I can benefit, and I think I can bring a lot, including a new point of view.”

As he advances his research at UMD, Bader hopes his broad experience in mathematics can continue to make an impact—and a difference.

“Every time I’m studying a new mathematical theory, I have this feeling that things are falling into the right place. It’s fantastic and I get excited all over again. I get a kick out of being able to describe mathematical theory so others can get excited about it too,” Bader explained. “What makes me most proud is the people around me and what they do and their success. My mission is not just to be a researcher, but also to be part of the success of my students.” 

Ron Peled: ‘Fired up’ by mathematics

Peled has been inspired by the challenges of math for as long as he can remember.

“I have always been excited about mathematics,” Peled explained. “What fires me up is curiosity. I’m always curious that a simple question is out there, we don’t know the answer and it seems like it’s something I can think about. And if I think hard enough, I can make progress and perhaps I can solve it. Just that fact has always fascinated me.”

With a strong foundation in statistical physics and probability theory, Peled has also made significant contributions in related areas including combinatorics, discrete mathematics and analysis.

“I study phase transition, which is a branch at the interface of probability theory, a mathematical subject that I specialize in, and statistical physics—and this now has some tradition in mathematics,” Peled said. “From a physical perspective, this discipline is about how the properties of materials emerge from the interactions of the microscopic particles that make up these materials. For instance, you boil water and at 100 degrees Celsius it becomes gas. But what happens to it on the microscopic level? This is an example of a phase transition.”

Born and raised in Israel, Peled earned his undergraduate degree from the Open University of Israel, going on to receive his master’s in mathematics from Tel Aviv University and his Ph.D. in statistics from UC Berkeley, where his dissertation received the Herbert Alexander Prize and Citation in Probability. After a Clay Liftoff Fellowship at the University of Wisconsin and a two-year fellowship at the Courant Institute of Mathematical Sciences at New York University, Peled returned to Tel Aviv University in 2010 to become a professor in the School of Mathematical Sciences. The author of dozens of scientific papers, Peled spent the last two years at Princeton University and the Institute for Advanced Study.

“I’m very excited about the opportunities that the University of Maryland is giving me and I’m very grateful to the Brin family for providing the support for this position. It will certainly allow me to focus more on my research and provide the best conditions for it,” Peled noted. “I’m looking forward to getting to know the other professors, students, and postdocs here and developing a group studying statistical physics and probability theory.”

Peled also sees an exciting future for the Brin Mathematics Research Center at UMD, which was launched to expand the university’s mathematics and statistics research and education programs and support visiting scholars, workshops and symposia, and summer programs.

“They opened the Brin Center here and there are workshops and summer schools all year round, and we think that this will propel the mathematics department here even further,” Peled said. “If one looks at the rankings, Maryland is in the top 20 now and it looks to be strongly improving toward the top 10.”

Written by Leslie Miller