Upcoming Seminars
SPEAKER: Mauro Maggioni, Bloomberg Distinguished Professor in Mathematics and Applied Mathematics and Statistics, Johns Hopkins University
February 19th, 2026
Armitage Hall – Room 124 during free period (12:45pm to 1:45pm)
and also on zoom https://rutgers.zoom.us/j/96442897289?pwd=bPsYXaIvu0BuaxSVQxU0VTLnRUSotm.1
Meeting ID: 964 4289 7289
Password: 854998
ABSTRACT: I will discuss recent results in two research directions at the intersection of scientific machine learning and modeling of dynamical systems.
First, we consider systems of interacting agents or particles, which are commonly used in models throughout the sciences, and can exhibit complex, emergent large-scale dynamics, even when driven by simple interaction laws. We consider the following inference problem: given only observations of trajectories of the agents in the system, can we learn the unknown laws of interactions? We cast this as an inverse problem, discuss when this problem is well-posed, construct estimators for the interaction kernels with provably good statistical and computational properties, even in the nonparametric estimation regime when only minimal information is provided about the form of such interaction laws. We also demonstrate numerically that the estimated systems can accurately reproduce the emergent behaviors of the original systems, even when the observations are so short that no emergent behavior was witnessed in the training data. We also discuss the case where the agents are on an unknown network, and we need to estimate both the interaction kernel and the network.
In the second part of the talk, I will discuss recent applications of deep learning in the context of digital twins in cardiology, and in particular the use of operator learning architectures for predicting solutions of parametric PDEs, or functionals thereof, on a family of diffeomorphic domains — the patient-specific hearts — which we apply to the prediction of medically relevant electrophysiological features of heart digital twins.
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SPEAKER: Nawaf Bou-Rabee, Professor, Rutgers University – Camden
March 12th, 2026
Armitage Hall – Room 124 during free period (12:45pm to 1:45pm)
and also on zoom https://rutgers.zoom.us/j/96442897289?pwd=bPsYXaIvu0BuaxSVQxU0VTLnRUSotm.1
Meeting ID: 964 4289 7289
Password: 854998
TITLE: NUTS for NUTS: Advances in No-U-Turn Sampling and the Future of Markov Chain Monte Carlo
ABSTRACT: Markov chain Monte Carlo (MCMC) is the standard approach for approximate sampling from probability distributions, yet the efficiency of classical algorithms often deteriorates in high dimensions or anisotropic geometries, exactly where the concentration of measure begins to dominate. In these regimes, probability mass collapses into thin shells, gradients and energies concentrate, and naïve random-walk exploration becomes wasteful. The No-U-Turn Sampler (NUTS) and its descendants have reshaped modern MCMC practice by learning local geometry on the fly, enabling efficient exploration even in complex, high-dimensional landscapes.
This talk revisits the mathematical foundations of NUTS and shows how they can be extended and unified within a broader adaptive framework. This perspective leads to new algorithms that preserve the self-tuning spirit of NUTS while extending its reach to curved and discrete geometries. I will share recent insights into why these methods mix so efficiently, drawing on ideas from geometry, probability, analysis, and concentration phenomena (e.g., how Hamiltonian trajectories track typical sets and how U-turn diagnostics implicitly detect concentrated “effective scales”). Along the way, we will see how No-U-Turn ideas are evolving from clever computational innovations into a principled theory of locally adaptive MCMC, bringing us closer to the long-standing goal of samplers that require minimal tuning and are provably efficient.
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SPEAKER: Cecilia Mondaini, Associate Professor, Drexel University
April 16th, 2026
Armitage Hall – Room 124 during free period (12:45pm to 1:45pm)
and also on zoom https://rutgers.zoom.us/j/96442897289?pwd=bPsYXaIvu0BuaxSVQxU0VTLnRUSotm.1
Meeting ID: 964 4289 7289
Password: 854998
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SPEAKER: Lorena Bociu, Professor, North Carolina State University
April 30th, 2026
Free period (12:45pm to 1:45pm)
On zoom https://rutgers.zoom.us/j/96442897289?pwd=bPsYXaIvu0BuaxSVQxU0VTLnRUSotm.1
Meeting ID: 964 4289 7289
Password: 854998
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