http://www-math.umd.edu/research/seminars.html http://www-math.umd.edu/research/seminars.html Mon, 20 Oct 2025 16:15:00 EDT Where: Kirwan Hall 3206
Speaker: Ricardo Nochetto (UMD (Math and IPST)) - https://www.math.umd.edu/~rhn/
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.
]]> http://www-math.umd.edu/research/seminars.html Mon, 27 Oct 2025 16:15:00 EDT Where: Kirwan Hall 3206
Speaker: Mohamadreza Fazel (NCI) - https://ccr.cancer.gov/staff-directory/mohamadreza-fazel
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.

]]> http://www-math.umd.edu/research/seminars.html Mon, 03 Nov 2025 16:15:00 EST Where: Kirwan Hall 3206
Speaker: John Baras (UMD (ECE, ISR, CS, ME)) - https://ece.umd.edu/clark/faculty/357/John-S-Baras
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.
]]> http://www-math.umd.edu/research/seminars.html Mon, 10 Nov 2025 16:15:00 EST Where: Kirwan Hall 3206
Speaker: Jacob Wenegrat (UMD (AOSC)) - https://aosc.umd.edu/people/wenegrat-jacob
Abstract: The ocean’s abyssal overturning circulation provides the long timescale memory of the climate system, with a round trip between the surface and the deep interior taking around 1000 years. Maintaining this slow overturning requires turbulent mixing at depth, providing a pathway for dense waters to return to the surface. The canonical picture of how this mixing occurs involves remotely generated internal waves reflecting, and breaking, along the bottom topography. However, recent work by our group suggests that low-frequency flows along topography may also provide novel pathways to mixing, both through their role in wave-mean-flow interactions and via the generation of small-scale hydrodynamic instabilities. Large-eddy simulations of flows interacting with bottom topography indicate the presence of symmetric instability (a 2D mixed gravitational-inertial instability of rotating flows), which is shown to facilitate a transition to turbulence both along the bottom and in topographic wakes behind seamounts and headlands. Similar conditions are also amenable to parametric subharmonic instability, a wave triad instability. Linear stability analysis, and nonlinear simulations, show that this also provides a pathway for the breakdown of internal waves into turbulence, a novel source of mixing along the bottom. These results will be summarized in the context of their potential unresolved effects in our understanding and modeling of the ocean circulation, and several related open questions (including available PhD projects) will be discussed.


]]> http://www-math.umd.edu/research/seminars.html Mon, 17 Nov 2025 16:15:00 EST Where: Kirwan Hall 3206
Speaker: Raghu Raghavan (UMD (School of Business/DOIT, ISR, CS)) - https://terpconnect.umd.edu/~raghavan/
Abstract: Last-mile delivery is a critical component of logistics networks, accounting for approximately 30%–35% of costs. As delivery volumes have increased, truck route times have become unsustainably long. To address this issue, many logistics companies, including FedEx and UPS, have resorted to using a “driver aide” to assist with deliveries. The aide can assist the driver in two ways. As a “jumper,” the aide works with the driver in preparing and delivering packages, thus reducing the service time at a given stop. As a “helper,” the aide can independently work at a location delivering packages, and the driver can leave to deliver packages at other locations and then return. Given a set of delivery locations, travel times, service times, jumper’s savings, and helper’s service times, the goal is to determine both the delivery route and the most effective way to use the aide (e.g., sometimes as a jumper and sometimes as a helper) to minimize the total routing time. We model this problem as an integer program with an exponential number of variables and an exponential number of constraints and propose a branch-cut-and-price approach for solving it. Our computational experiments are based on simulated instances built on real-world data provided by an industrial partner and a data set released by Amazon. The instances based on the Amazon data set show that this novel operation can lead to, on average, a 35.8% reduction in routing time and 22.0% in cost savings. More importantly, our results characterize the conditions under which this novel operation mode can lead to significant savings in terms of both the routing time and cost. Our computational results show that the driver aide with both jumper and helper modes is most effective when there are denser service regions and when the truck’s speed is higher (≥10 miles per hour). Coupled with an economic analysis, we come up with rules of thumb (that have close to 100% accuracy) to predict whether to use the aide and in which mode. Empirically, we find that the service delivery routes with greater than 50% of the time devoted to delivery (as opposed to driving) are the ones that provide the greatest benefit. These routes are characterized by a high density of delivery locations. https://pubsonline.informs.org/doi/epdf/10.1287/msom.2022.0211


]]> http://www-math.umd.edu/research/seminars.html Mon, 01 Dec 2025 16:30:00 EST Where: Kirwan Hall 3206
Speaker: Ramani Duraiswami (UMD (CS)) -
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).


]]> http://www-math.umd.edu/research/seminars.html Mon, 08 Dec 2025 16:15:00 EST Where: Kirwan Hall 3206
Speaker: Mike Kreisel (Garoux.com) - https://garoux.com/
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.

]]> http://www-math.umd.edu/research/seminars.html Mon, 02 Feb 2026 16:15:00 EST Where: Kirwan Hall 3206
Speaker: Lin Cheng (UMD (ME)) - https://sites.google.com/view/cheng-lab/home
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.
]]> http://www-math.umd.edu/research/seminars.html Mon, 09 Feb 2026 16:15:00 EST Where: Kirwan Hall 3206
Speaker: Paul Patrone (NIST) -
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.

]]> http://www-math.umd.edu/research/seminars.html Mon, 23 Feb 2026 16:15:00 EST Where: Kirwan Hall 3206
Speaker: Lizhen Lin (UMD (Math and Stat)) - https://blog.umd.edu/lizhen01/
Abstract: Deep learning has achieved groundbreaking performance in various application domains. Alongside its practical success, there has been a growing effort to explore the theoretical foundations of deep learning models. This talk will focus on the statistical foundations underlying deep neural network (DNN) models. From a statistical perspective, deep learning models can be largely viewed as a nonparametric function or distribution estimation problem, where the underlying function or distribution is parameterized by a DNN. In supervised settings, deep neural networks, including feedforward DNNs, are used for regression and classification tasks. For distribution estimation, deep generative models, where the generators or scores are modeled using DNNs, are the state-of-the-art deep learning models. Statistical theory provides insights into understanding why deep neural networks often outperform classical nonparametric models, and why and how these models perform exceptionally well in practice. Key insights include their ability to adapt to various intrinsic structures of the high-dimensional data, such as a lower-dimensional manifold structure, therefore circumventing the curse of dimensionality.

]]> http://www-math.umd.edu/research/seminars.html Mon, 02 Mar 2026 16:30:00 EST Where: Kirwan Hall 3206
Speaker: Alexander Xu (Department of Bio Engineering, UMD) - https://bioe.umd.edu/clark/faculty/1894/Alexander-Xu
Abstract: Cancer causes the cells of the body to shatter their well-defined roles, proliferate, and invade other tissues, leading to premature death. As a lapsed mathematician turned bioengineer, I lead a research group that studies cancer to propose novel therapies based on the spatial structure of tumor tissue. While there is no such thing as a "topological combinatorial construct" as far as I know, there is great significance in how different cells in our body are positioned in space and relative to each other. Our modern understanding of cancer proposes that a complex network of biological signals, partitioned into various cell types, is the fabric that frays and eventually dissolves in cancer. The functions woven into this fabric include immune cell control of diseased cells, secreted signals that attract and repel cells, and even a physical meshwork of collagenous and fibrotic material that impedes tumor and immune migration. Currently, my lab uses spatial molecular tools that can measure dozens of proteins and thousands of RNA biomolecules directly within intact tissue, allowing us to reconstruct the physical cellular architecture of tumors. We can use this information to characterize tumor tissue in depth and identify structures with predictive significance, based on the spatial cellular organization. However, the tools that we use to describe tumor structures are still simplistic, and our vocabulary is still limited when describe interacting fields of objects with hundreds to thousands of signals and properties. My goals for this seminar are to first present the structure and language of spatial biology data and its current applications, and then to recruit your minds to capture the underlying structures, patterns, and projections that will allow us to translate spatial data into actionable hypotheses to improve the treatment of cancer.
]]> http://www-math.umd.edu/research/seminars.html Mon, 09 Mar 2026 16:15:00 EDT Where: Kirwan Hall 3206
Speaker: Dionisios Margetis (UMD (Math and IPST)) - https://www.math.umd.edu/~diom/
Abstract: In recent years, there are intensive efforts to control quantum systems. In particular, electron systems in atomically thin materials, surfaces and interfaces are technologically appealing, with numerous applications in optoelectronics. Theoretical and experimental studies in this direction have focused on semiconductor heterojunctions, semiconductor-insulator interfaces as well as monolayer graphene and various related heterostructures. Despite the tremendous progress made in these contexts, some fundamental questions remain unresolved.

In this talk, I will formally discuss the dispersion of waves arising from charge density oscillations near a fixed plane in three spatial dimensions (3D) at zero temperature from
Partial-Differential-Equation (PDE) and linear-spectral-analysis perspectives. The goal is to describe the interplay of microscopic scales that include a binding length in the emergence of the surface plasmon (SP), a collective low-energy charge excitation in the vicinity of the plane.

The model is a time-dependent one-particle Hartree-type PDE in 3D that aims to provide a mean-field description of a confined interacting many-body quantum system. The linearization of this equation around the ground state yields a homogeneous integral equation for the wave function in the coordinate of the vertical direction. The existence of nontrivial solutions to this equation implies an SP dispersion relation, which non-linearly connects the temporal frequency and the wave number of charge oscillations near the plane. This relation is obtained exactly in closed form by a transform technique. In the strong binding limit, the classical SP dispersion law is recovered from the above result, in agreement with a hydrodynamic model based on a projected Euler-Poisson system.

]]> http://www-math.umd.edu/research/seminars.html Mon, 23 Mar 2026 16:15:00 EDT Where: Kirwan Hall 3206
Speaker: Nan Xu (UMD (Bio Engineering)) - https://bioe.umd.edu/clark/faculty/1901/Nan-Xu
Abstract: My research develops mathematically grounded methods for inference in high-dimensional biomedical imaging data, spanning functional brain dynamics (functional neuroimaging) and viral heterogeneity (cryo-EM). In neuroimaging, I model time-varying interactions among brain regions, moving beyond static and correlational connectivity to infer directed, spatiotemporally evolving network organization. These approaches support mechanistic interpretation and yield innovative biomarkers relevant to conditions such as post-concussive vestibular syndrome (PCVD). In structural virology, I study 3D reconstruction of virus particles from cryo-EM images. I develop symmetry-aware methods that preserve particle-specific asymmetry while enforcing global symmetry constraints across the population, improving reconstruction of virus(-like) particles such as bacteriophage HK97. The unifying theme is to exploit dynamics, constraints, and invariances for reliable inference under noise and heterogeneity.

]]> http://www-math.umd.edu/research/seminars.html Mon, 30 Mar 2026 16:15:00 EDT Where: Kirwan Hall 3206
Speaker: Vadim Karatayev (UMD (Biology)) - https://biology.umd.edu/people/vadim-karatayev
Abstract: As systems undergo bifurcations, transient dynamics can provide deep insights into the clockwork of nature and illuminate novel control pathways. I will show how transients involving saddle-node bifurcations reveal the mechanisms of climate change impacts on giant kelp forests, windows of opportunity in restoring ecosystems, and effective solutions to global warming. I will also highlight my lab's new directions to understand network dynamics and point out open mathematical problems associated with each question.


]]> http://www-math.umd.edu/research/seminars.html Mon, 06 Apr 2026 16:15:00 EDT Where: Kirwan Hall 3206
Speaker: Ming C Lin (UMD (CS)) - https://www.cs.umd.edu/~lin/
Abstract: In this talk, we present an overview of some of our recent works on the differentiable programming paradigm for learning, control, and inverse modeling. These include using dynamics-inspired, learning-based algorithms for detailed garment recovery from video and 3D human body reconstruction from single- and multi-view images, to differentiable physics for robotics, quantum computing and VR applications. Our approaches adopt statistical, geometric, and physical priors and a combination of parameter estimation, shape recovery, physics-based simulation, neural network models, and differentiable physics, with applications to virtual try-on and robotics. We conclude by discussing possible future directions and open challenges

]]> http://www-math.umd.edu/research/seminars.html Mon, 13 Apr 2026 16:15:00 EDT Where: Kirwan Hall 3206
Speaker: Jonathan Poterjoy (UMD (AOSC)) - https://poterjoy.com/
Abstract: Data assimilation in high-dimensional systems, such as numerical weather prediction, presents a formidable computational challenge. Operational centers routinely infer the probabilistic evolution of state vectors comprising more than a billion variables using physics-based models, noisy observations, and classic Bayesian filtering techniques. While many of these approaches rely on heavy approximations, recent advances make it feasible to move beyond rigid Gaussian assumptions for the prior. These non-Gaussian approaches are becoming increasingly attractive, as inexpensive surrogate models prove more effective at rapidly generating large Monte Carlo estimates of this density. Nevertheless, traditional likelihood estimation still relies on a well-defined measurement operator, or forward model, to link model states to observations, and considers uncertainty only in the form of an observation error covariance. In reality, this measurement process can be highly nonlinear, rely on incomplete physics, or remain fundamentally unknown.
To address this challenge, we present a suite of operator-free strategies that directly estimate likelihood functions from training data. These methods range from leveraging kernel mean embeddings to dynamically learn conditional distributions within a Reproducing Kernel Hilbert Space (RKHS) to employing probabilistic generative models such as conditional variational autoencoders (cVAEs). To explore the scalability of these techniques, we integrate them with contemporary filtering algorithms and assess their performance in a low-dimensional application that serves as an analog for weather forecasting and climate reconstruction. By weighing the trade-offs in accuracy and computational cost, this work describes a path toward implementation in next-generation Earth System models.

]]> http://www-math.umd.edu/research/seminars.html Mon, 20 Apr 2026 16:15:00 EDT Where: Kirwan Hall 3206
Speaker: Brian Hunt (UMD (MATH & IPST)) - https://terpconnect.umd.edu/~bhunt/
Abstract: Recent advances in machine learning have been successful at forecasting complex systems, such as the weather, with purely data-driven models. Here I will describe our group's research into hybrid modeling, combining machine learning with a physics-based model. Our goal is to use data to improve the model's skill both at short-term forecasting and at long-term "climate" simulation. I will include some results from applying the hybrid approach to model the earth's weather and climate.
]]> http://www-math.umd.edu/research/seminars.html Mon, 04 May 2026 16:15:00 EDT Where: Kirwan Hall 3206
Speaker: John Baras (UMD (ECE, ISR, CS, ME)) - https://ece.umd.edu/clark/faculty/357/John-S-Baras
Abstract: Robustness is a fundamental concept in systems science and engineering. It is a critical consideration in all inference and decision-making problems. It has recently surfaced again in the context of machine learning (ML), reinforcement learning (RL) and artificial intelligence (AI). We describe a novel and unifying theory of robustness for ML/RL/AI emanating from our much earlier fundamental results on robust output feedback control for general systems (including nonlinear, HMM and set-valued). We briefly summarize this theory and the universal solution it provides consisting of two coupled HJB equations. These earlier results rigorously established the equivalence of three seemingly unrelated problems: the robust output feedback control problem, a partially observed differential game, and a partially observed risk sensitive stochastic control problem. We first show that the “four block” view of the above results leads naturally to a similar formulation of the robust ML problem, and to a rigorous path to analyze robustness and attack resiliency in ML. Then we describe a recent risk-sensitive approach, using an exponential criterion in deep learning, that explains the convergence of stochastic gradients despite over-parametrization. Finally, we describe our most recent results on robust and risk sensitive RL for control, using exponential rewards, that emerge from our earlier theory, with the important new extension that the models are now unknown. We show how all forms of regularized RL can be derived from our theory, including KL and Entropy regularization, relation to probabilistic graphical models, distributional robustness. The deeper reason for this unification emerges: it is the fundamental tradeoff between performance and risk measures in decision making, via rigorous duality. We close with open problems and future research directions.
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