Professor Monica Visan Receives Inaugural Edmond and Nancy Tomastik Prize

UCLA Mathematics Professor Monica Visan has received the Inaugural Edmond and Nancy Tomastik Prize, presented by the American Mathematical Society. Visan is being honored for her contributions to nonlinear dispersive and wave equations, specifically through the introduction, development, and applications of the method of commuting flows.

She was honored at the Awards Celebration on January 5, 2026, during the 2026 Joint Mathematics Meetings in Washington, DC. At the same event, she also delivered the 2026 AWM-AMS Emmy Noether Lecture.

In response to receiving the award, Visan commented: “It is a great honor to receive the inaugural Edmond and Nancy Tomastik Prize in Differential Equations. I am sincerely grateful to the Tomastik family for their generous support in promoting research and scholarship in this field. I would like to thank the Prize Committee for this recognition. I am especially grateful to my collaborators, whose insight and partnership have been invaluable to my work.”

The Edmond and Nancy Tomastik Prize in Differential Equations is awarded for notable work in Differential Equations published during the preceding six years. The work must be published in a recognized, peer-reviewed venue. The prize is awarded every three years.

Read the official AMS release here.

Professors Guido Montúfar and Deanna Needell receive NSF AIMing grant

Professors Guido Montúfar and Deanna Needell have received a National Science Foundation grant on Artificial Intelligence, Formal Methods, and Mathematical Reasoning. This program (AIMing) supports research at the “interface of innovative computational and artificial intelligence (AI) technologies and new strategies/technologies in mathematical reasoning to automate knowledge discovery”. This grant is a collaboration between Cal Tech, BYU, and UCLA, and will advance AI with mathematical foundations, aiming for more interpretable, controllable, and trustworthy AI models.

Mathematics is uniquely positioned to drive major advances in AI because it is the foundational language of science, excels at rigorous abstraction, and offers deep, well-defined problems whose solutions may require automated reasoning. At the same time, mathematical research produces rich, structured datasets that are ideal for applying and testing modern AI and machine learning techniques. These datasets are inexpensive to generate, scalable in complexity, and rich in symmetry, making them powerful testbeds for developing models that can learn, reason, and generalize. Applying AI to mathematics can both accelerate progress on long-standing mathematical problems and motivate mathematicians to engage directly with AI system design, creating a mutually reinforcing bridge between advancing mathematics and foundational AI research.

Learn more about the grant here.

UCLA and Purdue Collaboration to Uncover the Mechanisms behind Memorization in Artificial Intelligence

Artificial Intelligence (AI) systems are becoming part of everyday life, utilized for generating and editing text, recommending content, and recently in exploring mathematical and scientific data. However, AI tools that memorize, that is, store and reproduce outcomes directly seen in their training sets, pose several risks related to security, privacy, and reliability. Through a $1M multi-year National Science Foundation’s Mathematical Foundations of Artificial Intelligence (MFAI) grant, Hayden Schaeffer (Professor of Mathematics, UCLA), Abolfazl Hashemi (Assistant Professor in Electrical and Computer Engineering at Purdue University), and Kaushik Roy (Edward G. Tiedemann Jr. Distinguished Professor of Electrical and Computer Engineering at Purdue University) will investigate how and why AI models memorize data.

When AI models are trained, they learn patterns from large datasets. The hope is that with a large and rich enough training dataset, the learned model will be able to generalize, that is, will be able to predict outcomes from unseen settings. However, with larger models a potential risk is that the AI systems memorize parts of their training data, which can lead to serious issues. For example, if a model retains sensitive data, it could potentially reveal private or secure information through its outputs. Memorization may also contribute to models that perform poorly when applied to new (unseen) inputs. Although observed in large language models (LLMs), the mathematical, statistical, and computational underpinnings of memorization remain poorly understood.

This newly funded research effort aims to address memorization by developing mathematical and statistical techniques to detect and quantify memorization during training. The research team will formalize the quantitative aspects of memorization and construct computationally tractable indicators to detect when and how memorization occurs. This work is at the intersection of mathematics, statistics, and computer science, utilizing and developing methods in optimization, information theory, and dynamics. By understanding subtleties in the training of AI systems, this research could help to ensure that these systems lead to positive societal and scientific outcomes.

Learn more about the program here.

The award is split into two parts, learn more below:

https://www.nsf.gov/awardsearch/show-award?AWD_ID=2502561

https://www.nsf.gov/awardsearch/show-award?AWD_ID=2502560

Monica Visan Named 2026 AWM-AMS Emmy Noether Lecturer

UCLA Mathematics Professor Monica Visan has been selected to deliver the 45th Emmy Noether Lecture at the Joint Mathematics Meetings held in Washington, D.C. from January 4–7, 2026. This distinguished lecture was established in 1980 by the Association for Women in Mathematics (AWM) to honor the extraordinary contributions of women in the field of mathematical sciences.

“Visan is a leading figure in the field of nonlinear dispersive equations, having made significant contributions to the well-posedness theory of critical dispersive equations, the study of dispersive equations on domains with boundaries, and the construction of invariant measures for several key models. In collaboration with Rowan Killip, she introduced the method of commuting flows (Annals of Math. (2) 190 (2019)), which has led to important breakthroughs in the analysis of integrable dispersive models, including the Korteweg-de Vries equation.”

Learn more about the Emmy Noether lecture here.

Read the official AMW release here.

Professor Andrea Bertozzi named Ulam Scholar at Los Alamos National Laboratory

UCLA Mathematics Professor Andrea Bertozzi has been named a 2026 Ulam Scholar at the Los Alamos National Laboratory. As an Ulam Scholar, Bertozzi will be presenting a series of talks on her research during a short-term residency at the Center for Nonlinear Studies (CNLS) at Los Alamos National Laboratory.

“The Stanislaw M. Ulam Distinguished Scholar is an annual award which enables a noted scientist to spend time at the Center for Nonlinear Studies (CNLS) at Los Alamos National Laboratory carrying out research in collaboration with staff scientists of the Laboratory. The Ulam Scholarship honors the memory of the brilliant Polish-American mathematician Stan Ulam, who was among the founders of what has now become “nonlinear science.” A number of Ulam Scholars, from 1985 until the present, have made significant contributions to Laboratory efforts in nonlinear science and established sustained collaborations with researchers in the technical divisions.”

Learn more about the Ulam Scholar here.