Keynote Speaker

Dr. Jack Xin

Professor Xin received Ph.D. degree in mathematics from the Courant Institute of Mathematical Sciences, New York University, in 1990. He was a faculty member with The University of Arizona, from 1991 to 1999, and The University of Texas at Austin, from 1999 to 2005. He is currently a Distinguished and Chancellorâ€™s Professor of mathematics with UC Irvine. His research interests include applied analysis and computational methods, and their applications in multi-scale problems, data science and AI. He is a fellow of the Guggenheim Foundation, American Mathematical Society, American Association for the Advancement of Science, the Society for Industrial and Applied Mathematics, and Asia-Pacific Artificial Intelligence Association. He was a recipient of Qualcomm Faculty Award, from 2019 to 2022 and Qualcomm Gift Award, from 2023 to 2025. He was elected as a member of the National Academy of Artificial Intelligence in 2025. He received the Academy's Distinguished AI Scholar Award in 2025.

Title: Stochastic Interacting Particle Methods and Generative Learning for Multiscale PDEs

Abstract: Multiscale time dependent partial differential equations (PDE) are challenging to compute by mesh based methods especially when their solutions develop large gradients or concentrations at unknown locations. We discuss stochastic interacting particle (SIP) methods for advection-diffusion-reaction

PDEs based on probabilistic representations of solutions, and show their self-adaptivity and efficiency in several space dimensions. Using SIP solutions as training data, we compare generative models (such as optimal transport, diffusion, flow-matching and one-step diffusion) in learning, interpolating and predicting solutions as physical parameters vary.

Dr. Stanley Osher

Professor Osher received his PhD in Mathematics from New York University in 1966. He has been at UCLA since 1976. He now is a Professor of Mathematics, Computer Science, Electrical Engineering and Chemical and Biomolecular Engineering .He has been elected to the US National Academy of Science, the US National Academy of Engineering and the American Academy of Arts and Sciences. He was awarded the SIAM Pioneer Prize at the 2003 ICIAM conference and the Ralph E. Kleinman Prize in 2005. He was awarded honorary > doctoral degrees by ENS Cachan, France, in 2006 and by Hong Kong Baptist University in 2009. He is a SIAM and AMS Fellow. He gave a one hour plenary address at the 2010 International Conference of Mathematicians. He also gave the John von Neumann Lecture at the SIAM 2013 annual meeting. He is a Thomson-Reuters/ Clarivate highly cited researcher-among the top 1% from 2002-present in both Mathematics and Computer Science with an h index of 133 and 164,000 citations. This makes him one of the world's most productive researchers in these fields. In 2014 he received the Carl Friedrich Gauss Prize from the International Mathematics Union-this is regarded as the highest prize in applied mathematics. In 2016 he received the William Benter Prize. His current interests involve data science, which includes optimization, image processing, compressed sensing , machine learning, neural nets and applications of these techniques in physics, engineering and elsewhere

Title: A Characteristic-Based Deep Learning Framework for Hamilton–Jacobi Equations with Application to Optimal Transport

Abstract: We present a highly efficient methodology for solving Hamilton–Jacobi (HJ) PDEs and demonstrate its effectiveness in the context of optimal transport (OT). Our approach begins with the derivation of a novel implicit solution formula for HJ PDEs, grounded in the method of characteristics and and closely related to classical Hopf-type formulations. Building upon this foundation, we propose a deep learning-based framework that computes viscosity solutions without reliance on supervised training data. By leveraging the mesh-free nature and expressive capacity of neural networks, the proposed method enables scalable, accurate, and computationally efficient solutions to high-dimensional and non-convex HJ PDEs.

Furthermore, we show that this framework enables the construction of efficient OT models. By exploiting the characteristic structure of HJ PDEs, we demonstrate that the bidirectional OT map admits a closed-form representation through the solution of an associated HJ equation. Leveraging this insight, we develop a deep learning model that directly computes OT maps using the implicit solution formula, thereby eliminating the need for numerical integration of ODEs. This characteristic-driven formulation leads to substantial improvements in both the accuracy of the computed transport maps and the efficiency of the sampling process.

Dr. Yufeng Liu

Professor Liu is a Professor of Statistics, at the University of Michigan at Ann Arbor. He received his Ph.D. in Statistics from Ohio State University in 2004. His current research interests include high-dimensional data analysis, machine learning, nonparametric statistics, bioinformatics, and cancer genomics.

Title: Statistical Significance of Clustering for High-Dimensional Continuous and Count Data

Abstract: Clustering is widely used in biomedical research for identifying meaningful subgroups. However, most existing clustering algorithms do not account for the statistical uncertainty inherent in the resulting clusters, which can lead to spurious findings due to natural sampling variation. To address this issue, the Statistical Significance of Clustering (SigClust) method was developed to formally assess the significance of clusters in high-dimensional data. In this talk, we begin by defining a cluster as a group of observations originating from a single Gaussian distribution and framing the evaluation of clustering significance as a hypothesis testing problem. We will then discuss challenges related to high-dimensional covariance estimation in SigClust and introduce an enhanced version that incorporates multidimensional scaling (MDS) on dissimilarity matrices to address the challenge. To extend the methodology beyond continuous data, we propose SigClust-DEV, a recent approach designed to assess the significance of clustering in count data. Finally, we will illustrate the application of SigClust to single-cell RNA sequencing (scRNA-seq) data and electronic health records (EHRs) to demonstrate its utility in real-world biomedical contexts.

Dr. Ron Buckmire

Profesor Buckmire is the Dean of the School of COmputer Science and Mathematics and Professor of Mathematics at Marist University. He was on the faculty of Occidental College for more than 30 years after receiving a Ph.D. in Mathematics from Rensselaer Polytechnic Institute in 1994. There he served as department chair, associate dean for curricular affairs and director of the Core (general education) program and taught classes such as Calculus, History of Mathematics, Differential Equations, and Mathematical Modeling.

He has served as a Program Officer in the Division of Undergraduate Education at the National Science Foundation twice (2011-2013 and 2016-2018). His published articles are in an eclectic collection of peer-reviewed journals such as Data, Notices of the American Mathematical Society, Numerical Methods for Partial Differential Equations, Journal of Humanistic Mathematics, Journal of Machine Learning for Modeling and Computing, andAlbany Law Review.

In 2023, he was named a Fellow of the Society for Industrial and Applied Mathematics (SIAM), the first person from a small liberal arts college, the fourth Black person, and the first openly LGBTQ+ person to receive this prestigious honor. He was SIAM’s inaugural Vice-President for Equity, Diversity, and Inclusion from 2021 to 2024.

Title: 'To Be Real’: Strategies For Increasing Student Engagement in Computational, Mathematical, and Statistical Courses

Abstract: One of the primary goals of any educational program is to provide students with experiences that result in producing students who have met the program’s learning outcomes and program objectives. An important factor in student success is student engagement. In this talk, Dr. Buckmire will provide examples of justice-oriented activities, questions, and projects that are suitable for different kinds of quantitative (computational, mathematical, or statistical) courses that may lead to increased student engagement in computational, mathematical, and statistical courses by adding course content that is resonant, relevant, and ‘real.’

Dr. Dean Lee

Professor Lee is a professor of physics at the Facility for Rare Isotope Beams (FRIB) and the Department of Physics and Astronomy at Michigan State University and department head of Theoretical Nuclear Science at FRIB

Lee's research interests include superfluidity, nuclear clustering, nuclear structure from first principles calculations, ab initio scattering and inelastic reactions, and properties of nuclei as seen through electroweak probes. He also works on new technologies and computational paradigms such as eigenvector continuation, machine learning tools to find correlations, and quantum computing algorithms for the nuclear many-body problem.

Title: Introduction to Quantum Computing and Activities at Michigan State University

Abstract: The first half of this talk will be an introduction to quantum computing. Dr Lee will discuss the basic concepts and methods, survey the quantum computing hardware currently available, and explain the current challenges in the field. Then Dr. Lee will review some of the activities at Michigan State University in quantum computing and related areas..

Dr. Aram Harrow

Dr. Harrow grew up in Michigan before attending MIT for his undergraduate (math and physics, 2001) and graduate (physics, 2005) degrees. He then served as a lecturer in the math and computer science departments of the University of Bristol for five years, and as a research assistant professor at the University of Washington for two years. In 2013, he joined the MIT Physics department as an assistant professor. He was promoted to Associate Professor with tenure in 2018 and to full Professor in 2022.

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