Weekly Seminars

Date: Monday, February 2, 2026

Time: 4:15 PM

Place: MATH 3206 (Colloquium Room)

Speaker: Lin Cheng (ME)

Title: Scientific Artificial Intelligence (Sci-AI) for Advanced Materials’ Law Discovery, Modeling, and Design - AMSC

Abstract: Advanced manufacturing enables rapid fabrication of complex materials and structures, allowing exploration of large design spaces in materials composition, microstructure, and processing conditions. However, the high dimensionality of these spaces, together with manufacturing uncertainties and defects, poses major challenges for traditional computational science and engineering methods. Addressing these challenges requires new approaches that integrate multi-modal data with physics-based modeling and artificial intelligence to accelerate scientific discovery and materials innovation. This seminar introduces Scientific Artificial Intelligence (Sci-AI) approaches for discovering materials constitutive laws, modeling complex materials behavior, and designing new materials with targeted properties. The central theme is the tight coupling of physical principles, data-driven learning, and interpretability, enabling AI models to move beyond black-box prediction toward reliable scientific inference. The talk is organized around three interconnected themes. First, a hierarchical symbolic AI framework is presented for the automated discovery of physically interpretable constitutive laws directly from data, allowing the model to identify governing mechanisms, enforce dimensional consistency and physical constraints, and balance accuracy with model complexity. Second, a physics-informed, image-based encoder–decoder architecture is introduced to accelerate materials behavior modeling by learning compact latent representations of complex microstructural and field data, while seamlessly fusing high-fidelity simulations, in-situ imaging, and external sensing information across multiple length and time scales. Third, a generative AI framework is developed for inverse materials design, enabling the synthesis of physically plausible microstructures conditioned on nonlinear material properties, processing constraints, and performance targets, thereby closing the loop from data and modeling to design and manufacturing. Overall, this seminar highlights recent advances in Sci-AI for computational science and engineering and demonstrates how physics-guided machine learning can bridge data and models to enable advanced materials modeling and design.

Date: Monday, February 9, 2026

Time: 4:15 PM

Place: MATH 3206 (Colloquium Room)

Speaker: Paul Patrone (NIST)

Title: A Unified Theory of Machine Learning through Probabilistic Consistency - AMSC

Abstract: With the growing adoption of machine-learning (ML) tools, there is an ever increasing need to develop rigorous methods for assessing the quality of their predictions and outputs. Despite this, fundamental questions about the connection between ML and probability remain unresolved. For example, do arbitrary ML models always have probabilistic interpretations? What does it mean for a ML model to be consistent with probability? And how could one extract probabilities from “hard” classifiers such as support vector machines?
In this talk, I will address these questions by deriving a level-set theory of classification that establishes an equivalence between certain types of self-consistent ML models and class-conditional probability distributions. I begin by considering the properties of binary Bayes classifiers, recognizing that the boundary sets separating classes can be re-interpreted as level-sets of density ratios, which quantify the relative probability that a sample point belongs to a given class. I then demonstrate how these level sets can be ordered in terms of an affine parameter related to the prevalence (fraction of elements in a class). This analysis subsequently implies that all Bayes classifiers have monotonicity and self-consistency properties, the latter being equivalent to the law of total probability. By reversing the analysis, I then discuss how for any classifier, the monotonicity and self-consistency properties (along with a normalization condition) imply the existence of probability distributions for which the classifier is in fact Bayes optimal. This allows one to determine when classifiers can be equipped with probabilistic interpretations, and it yields the density ratios via the level-set theory. Throughout, I illustrate these ideas in the context of real-world examples from diagnostics and image analysis.

Date: Monday, February 23rd, 2026

Time: 4:15 PM

Place: MATH 3206 (Colloquium Room)

Speaker: Lizhen Lin (MATH/STAT)

Title: TBD

Abstract: TBD

Date: Monday, October 20, 2025

Time: 4:15 PM

Place: MATH 3206 (Colloquium Room)

Speaker: Ricardo Nochetto (Math, IPST)

Title: Liquid Crystal Networks: Modeling, Approximation, and Computation

Abstract: We discuss modeling, numerical analysis and computation of liquid crystal networks (LCNs). These materials couple a nematic liquid crystal with a rubbery material. When actuated with heat or light, the interaction of the liquid crystal with the rubber creates complex shapes. Thin bodies of LCNs are thus natural candidates for soft robotics applications. We start from the classical 3D trace energy formula and derive a reduced 2D membrane energy as the formal asymptotic limit of vanishing thickness, including both stretching and bending energies, and characterize the zero energy deformations. We design a sound numerical method and discuss its Gamma convergence. We present computations showing the geometric effects that arise from liquid crystal defects as well as computations of non-isometric origami within and beyond theory. This work is joint with the former students L. Bouck and S. Yang, and the current student G. Benavides.

Date: Monday, October 27, 2025

Time: 4:15 PM

Place: MATH 3206 (Colloquium Room)

Speaker: Mohamadreza Fazel (NCI)

Title: Application of Bayesian frameworks in Fluorescence Microscopy and Optics

Abstract: Fluorescence microscopy and single-molecule fluorescent methods have played a crucial role in shedding light on various subcellular mechanisms and providing insights into different subcellular structures and their functions. However, these techniques still face multiple challenges in data analysis, including high photon budget requirements, rigorous noise treatment, model selection, and others. In this seminar, I will discuss my research on leveraging tools from Bayesian framework to address questions in single-molecule localization microscopy, particle tracking and spectral imaging.

Date: Monday, November 3, 2025

Time: 4:15 PM

Place: MATH 3206 (Colloquium Room)

Speaker: John Baras (ECE, ISR, CS, ME)

Title: Universal Architectures for Progressive Machine Learning: Model, Performance Evaluation, Applications

Abstract: The “One Learning Algorithm Hypothesis” summarizes strong experimental evidence that the human brain processes visual, acoustic, and haptic signals, for perception and cognition with a workflow that corresponds to the same abstract architectural model. We present our most recent results on the development and performance evaluation of a universal machine learning architecture inspired by this hypothesis. The abstract architecture proposed is comprised of a multi-resolution processing front, followed by a feature extractor, followed by two “local” learning modules (first an unsupervised one, followed by a supervised one), followed by a deterministic annealing module. There are two global feedback loops, one to the multiresolution processor and one to the feature extractor. Innovative analytical methods and results include: multi-resolution hierarchy, use of Bregman divergences as dissimilarity measures, multi-scale stochastic approximation, multi-scale approximation to Bayes decision surfaces, optimization-information duality. We demonstrate the superior performance and characteristics of the resulting algorithms including: domain agnostic, on-line progressive learning, interpretability, robustness to noise and adversarial attacks, computable performance-complexity tradeoff. We present several applications in signal processing, graph problems, estimation and control.

Date: Monday, November 10, 2025

Time: 4:15 PM

Place: MATH 3206 (Colloquium Room)

Speaker: Jacob Wenegrat (AOSC)

Title: Fluid dynamics in the abyss: waves, instabilities, and the mixing of the deep ocean

Date: Monday, November 17, 2025

Time: 4:15 PM

Place: MATH 3206 (Colloquium Room)

Speaker: Raghu Raghavan (BMGT/ISR)

Title: The Driver-Aide Problem: Coordinated Logistics for Last-Mile Delivery

Date: Monday, December 1, 2025

Time: 4:30 PM

Place: MATH 3206 (Colloquium Room)

Speaker: Ramani Duraiswami (Computer Science)

Title: Scientific Computing with/for Machine Learning

Abstract: Machine learning (ML) has recently significantly advanced many domains like natural language processing, computer vision, and speech processing. Since the arrival of ChatGPT in late 2022, generative models have accelerated this rate of progress. ML tools now affect all aspects of the scientific computing endeavor, and themselves can be helped with scientific computing. I will first briefly describe various related themes of research currently underway in my group at UMD: Differentiable Modeling, Implicit Representations, the Attention mechanism in Transformer architectures, and Large Audio Language Models (a recent talk on LALMs). This talk will focus on the use of Differentiable Models and Implicit Neural Representations in scientific modeling. Scientists previously developed forward numerical models encoding domain knowledge. Making these models differentiable allows for this knowledge to be incorporated in deep learning architectures, and allows achieving more efficient computational pipelines for tasks like parameter optimization, inverse problems, and explainable models in data-sparse domains. I will talk about our recent work in a differentiable model for human hearing (with Leslie Famularo & Nishit Anand), room acoustics (Bowen Zhi & Armin Gerami), signal processing for spatial audio (Armin Gerami), computer graphics via Gaussian Splatting and via a regularized SDF formulation (Meenakshi Krishnan), and inverse problems in mathematical physics (Meenakshi Krishnan and Pranav Pulijala).

Date: Monday, December 8, 2025

Time: 4:15 PM

Place: MATH 3206 (Colloquium Room)

Speaker: Mike Kreisel (Garoux)

Title: A Math PhD's Journey Through Industry Into Entrepreneurship

Abstract: I graduated with my math PhD from UMD in 2015. Since then I've taken a tour of non-academic jobs, working for a startup (Quantifind), Fortune 500 companies (Google, Comcast), government (Presidential Innovation Fellows) and eventually starting my own company (Garoux). I will present my thoughts on the pros and cons of these different career paths from the perspective of a math student. I'll also introduce my company Garoux and discuss our work building AI tools to reduce burden and burnout for government workers.

Date: Monday, February 24, 2025

Time: 4:15 PM

Place: MATH 3206 (Colloquium Room)

Speaker: Maria Cameron (MATH)

Title: Complex Dynamics of Nonlinear Oscillators and Their Applications

Abstract: Nonlinear oscillators have a broad range of applications in engineering, including rotors, energy harvesters, sensors, and precision timing devices. The dynamics of a single oscillator with cubic nonlinearity and external periodic forcing is surprisingly rich. Depending on parameters, it may admit multiple attractors that may be periodic or chaotic. Their basin boundaries may be fractal. Linking oscillators into arrays and adding noise further complicates their dynamics. I will discuss a method for finding the most probable escape paths from the basins of attractors of noisy oscillators, sensor design, and a few open mathematical problems related to nonlinear oscillators.

Date: Monday, March 3, 2025

Time: 4:15 PM

Place: MATH 3206 (Colloquium Room)

Speaker: Steven Gabriel (Mechanical Engineering)

Title: Optimization/Equilibrium Modeling & Algorithm Development for Infrastructure Planning: Focus on Energy, Water and Transport Summary of Research and Teaching

Abstract: Professor Gabriel’s research group develops models, theory, and algorithms for solving problems that arise in infrastructure planning such as: energy, water, transport. These models are typified by a set of autonomous agents (i.e., energy market participants, vehicles) that share a common network. The equilibrium aspects arise since each of the players or subsets of the players compete non-cooperatively with each other for the infrastructure network’s resources. The concatenation of all these optimization problems as well as any system-level constraints results in what is known as an equilibrium problem; typically called a mixed complementarity problem (MCP) or a variation inequality (VI). Such problems generalize the Karush-Kuhn-Tucker (KKT) conditions of nonlinear programs, Nash-Cournot games, as well as many other problems in operations research, engineering and economic systems. These equilibrium problems can also be single-level, wherein all the agents are at the same level or such problems can be multi-level. In the latter case, some famous paradigms include: bilevel optimization (e.g., Stackelberg leader-follower games), attacker-defender interdiction problems and trilevel optimization. Please see Professor Gabriel’s website for further details: http://www.stevenagabriel.umd.edu/ or email him directly at  with any questions you might have.

Date: Monday, March 10, 2025

Time: 4:15 PM

Place: MATH 3206 (Colloquium Room)

Speaker: Elana Fertig (School of Medicine)

Title: Forecasting carcinogenesis

Abstract: This talk presents a hybrid mathematical modeling and bioinformatics strategy to uncover interactions between neoplastic cells and the microenvironment during carcinogenesis and therapeutic response. As pancreatic cancer develops, it forms a complex microenvironment of multiple interacting cells. The microenvironment of advanced cancer includes a dense composition of cells, such as macrophages and fibroblasts, that are associated with immunosuppression. New single-cell and spatial molecular profiling technologies enable unprecedented characterization of the cellular and molecular composition of the microenvironment. These technologies provide the potential to identify candidate therapeutics to intercept immunosuppression. Inventing new mathematical approaches in computational biology are essential to uncover mechanistic insights from high-throughput data for these precision interception strategies. Here, we demonstrate how converging technology development, machine learning, and mathematical modeling can relate the tumor microenvironment to carcinogenesis and therapeutic response. Combining genomics with mathematical modeling provides a forecast system that can yield computational predictions to anticipate when and how the cancer is progressing for therapeutic selection. This mathematical forecast system will empower a new predictive oncology paradigm, which selects therapeutics to intercept the pathways that would otherwise cause future cancer progression.

Date: Monday, March 31, 2025

Time: 4:15 PM

Place: MATH 3206 (Colloquium Room)

Speaker: Haizhao Yang (Mathematics)

Title: Finite Expression Method: A Symbolic Approach for Scientific Machine Learning 

Abstract: Machine learning has revolutionized computational science and engineering with impressive breakthroughs, e.g., making the efficient solution of high-dimensional computational tasks feasible and advancing domain knowledge via scientific data mining. This leads to an emerging field called scientific machine learning. In this talk, we introduce a new method for a symbolic approach to solving scientific machine learning problems. This method seeks interpretable learning outcomes via combinatorial optimization in the space of functions with finitely many analytic expressions and, hence, this methodology is named the finite expression method (FEX). It is proved in approximation theory that FEX can efficiently learn high-dimensional complex functions. As a proof of concept, a deep reinforcement learning method is proposed to implement FEX for learning the solution of high-dimensional PDEs and learning the governing equations of raw data.

Date: Monday, April 7, 2025

Time: 4:15 PM

Place: MATH 3206 (Colloquium Room)

Speaker: Bill Fagan (Biology)

Title: Finite Expression Method: A Symbolic Approach for Scientific Machine Learning 

Abstract: This seminar will provide an overview of how continuous stochastic processes have been applied to the study of animal movement ecology using data from GPS tracking devices. I will present the mathematical foundations of these applications and discuss how we statistically fit the stochastic process models to diverse biological datasets. I will then give an overview of the wide range of applications that my colleagues and I have found for these approaches, including such biological topics as:
1) animal home ranges, migration, and space use
2) behavioral evidence for learning and disease states
3) route-based movement by carnivores
4) consumer-resource interactions

Movement data from GPS tracking devices typically feature a high degree of temporal autocorrelation, often at multiple scales. Over the years, our work has dealt with such data in a variety of statistical contexts, including:
1) timeseries analysis
2) kernel density estimation
3) path estimation via kriging
4) estimation of probability ridges
5) comparative (i.e., phylogenetically controlled) analyses
The talk will present results from joint work with mathematicians Leonid Koralov and Mark Lewis; past-postdocs Christen Fleming, Eliezer Gurarie, and Michael Noonan; past-PhD students Justin Calabrese and Nicole Barbour; current PhD students Frank McBride, Marron McConnell, Gayatri Anand, Stephanie Chia, Qianru Liao, and Phillip Koshute; current undergraduate Zachary Tomares; and hundreds of biologists. Open questions abound and span a wide range of difficulty. I have access to mountains of animal movement data and am eager for collaborators.

Date: Monday, April 14, 2025

Time: 4:15 PM

Place: MATH 3206 (Colloquium Room)

Speaker: Antony Jose (Cell Biology & Molecular Genetics)

Title: Structure of regulatory information in living systems

Abstract:  Regulatory architectures can persist despite turnover of the constituent molecules. The recreation of such architectures at the start of each generation drives heredity. We enumerated and analyzed the 26 simplest architectures that form a basic alphabet (A to Z) of motifs capable of indefinitely transmitting heritable information [1]. The topology of these architectures represents information that can ‘mutate’ through epigenetic changes. Here I highlight two recent applications of these insights in the nematode C. elegans. One, the transgenerational dynamics of experimentally observed RNA-mediated epigenetic changes can range from silencing that lasts for >250 generations to recovery from silencing within a few generations and subsequent resistance to silencing [2]. Tuning of positive feedback loops can explain these observations and provide quantitative predictions for generating heritable epigenetic changes of defined durations [1]. Two, the prevalence of homeostasis in living systems suggests that the topologies of regulatory architectures frequently enable compensatory feedback. Consistently, we identified new regulators of RNA silencing by using AlphaFold to predict protein-protein interactions between known regulators of RNA silencing and proteins encoded by frequently perturbed mRNAs [3]. These discoveries underscore the necessity and utility of considering the topological constraints of regulatory architectures that arise from two universal properties of living systems - heredity and homeostasis.

[1] Jose AM (2024) eLife, 12:RP92093.
[2] Devanapally et al. (2021) Nature Communications, 12: 4239.
[3] Lalit F and Jose AM, (2025) Nucleic Acids Research, 53: gkae1246.

Date: Monday, April 21, 2025

Time: 4:15 PM

Place: MATH 3206 (Colloquium Room)

Speaker: Harry Dankowicz (Mechanical Engineering)

Title: Parameter Continuation and Uncertainty Quantification Near Stochastically Perturbed Limit Cycles and Tori

Abstract: This talk shows the use of parameter continuation techniques to characterize intermediate-term dynamics due to the presence of small Brownian noise near normally-hyperbolic, transversally stable periodic orbits and quasiperiodic invariant tori found in the deterministic limit. The proposed formulation relies on adjoint boundary-value problems for constructing continuous families of transversal hyperplanes that are invariant under the linearized deterministic flow, and covariance boundary-value problems for describing Gaussian distributions of intersections of stochastic trajectories with these hyperplanes. Analytical and numerical results, including validation with the help of the continuation package COCO, show excellent agreement with stochastic time integration for problems with either autonomous or time-periodic drift terms.

Date: Monday, May 5, 2025

Time: 4:15 PM

Place: MATH 3206 (Colloquium Room)

Speaker: Alexander Estes (School of Business)

Title: Stochastic Integer Programming with Limited Revisions

Abstract: We provide a framework that provides higher predictability in multi-stage integer programming. In this framework, a plan is produced at the start of the problem for the actions that will be taken in all stages of the problem. This plan can be revised in response to revealed uncertainty, but a limit is placed on the number of times that such revisions can be made. We develop integer programming formulations for this restriction. The improvements in predictability provided by this framework may come at the cost of a less optimal primary objective value, but theoretical and computational results indicate that the restriction on the number of revisions often only moderately affects the costs incurred in the optimization problem.