Publications

@article{nokey,

title = {Predicting the When: Multimodal AI for Time-to-Recurrence Analysis After Atrial Fibrillation Ablation},

author = {Minglang Yin, Changxin Lai, Ritu Yadav, Jenna A. Milstein, Linh Thi My Tran, Calvin O’Donnell, Sarah Schumacher, Craig Cronin, Robert Weinstein, Carolyna Yamamoto Alves Pinto, Zan Ahmad, Shiyi Chen, Arthur Lefebvre, Jooyoung Ryu, Audrey Lacy, Alana Thi Yee, Jiwoo Noh, Eugene Kholmovski, Mauro Maggioni, Hugh Calkins, David Spragg, Natalia Trayanova},

doi = {https://doi.org/10.64898/2026.05.12.26353055},

year  = {2026},

date = {2026-05-15},

urldate = {2026-05-15},

journal = {MedRXiv},

abstract = {MARTA-AF delivers individualized, time-varying AF recurrence risk forecasts and enables stratification into clinically meaningful risk groups. This framework has the potential to transform post-ablation management into a proactive paradigm and to support informed clinical decision-making prior to ablation.},

keywords = {Artificial Intelligence, cardiac electrophysiology, Machine learning, statistics},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{nokey,

title = {Probabilistic Cardiac Digital Twins for Robust Patient-Specific Modeling},

author = {Dimitris G Giovanis, Kelly Zhang, Justin Tso, Mauro Maggioni, Ioannis G Kevrekidis, Natalia Trayanova},

url = {https://www.biorxiv.org/content/10.64898/2026.05.07.723610v1},

doi = {https://doi.org/10.64898/2026.05.07.723610},

year  = {2026},

date = {2026-05-12},

urldate = {2026-05-12},

journal = {biorxiv},

abstract = {Uncertainty quantification (UQ) in computational heart models is essential for reliable cardiac digital twins (DTs) in personalized medicine, yet remains challenging. Traditional Monte Carlo and stochastic Galerkin methods often become impractical in the high-dimensional, nonlinear state variable and parameter spaces of cardiac electrophysiology and mechanics. This article introduces a framework for learning a joint probability density over cardiac observables and model parameters, enabling the characterization of statistical dependencies across a large number of variables in patient-specific cardiac DTs. By sampling from this density and conditioning on available data, useful predictive distributions can be constructed, allowing uncertainty to be propagated through the model and quantified in terms of variability. Conditional regression can then be performed directly on this learned density, enabling systematic exploration of interdependencies among observables for both predictive inference and model design. The statistical methodology adopts a geometry-aware generative learning framework, recently introduced by the authors, that decouples the learning of data geometry from sampling. First it identifies a low-dimensional latent representation that captures the intrinsic structure of the data and its multiscale geometric features. A stochastic differential equation is then formulated directly in the low-dimensional latent space to generate samples efficiently; these are subsequently mapped back to the high-dimensional space of cardiac states and parameters through a smooth lifting operator. We demonstrate the approach on a ventricular arrhythmia prediction benchmark, where the learned joint probability density enables the construction of predictive distributions over key parameters (e.g., conductivities, fibrosis patterns) through sampling and conditioning. This enables uncertainty to be propagated and quantified through sampling and conditioning on the learned joint density, with substantially fewer model evaluations than conventional UQ methods.},

keywords = {cardiac electrophysiology, diffusion geometry, digital twins, Machine learning, uncertainty quantification},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{DIMON2024,

title = {A scalable framework for learning the geometry-dependent solution operators of partial differential equations},

author = {Minglang Yin and Nicolas Charon and Ryan Brody and Lu Lu and Natalia Trayanova and Mauro Maggioni},

url = {https://arxiv.org/pdf/2402.07250.pdf

https://www.nature.com/articles/s43588-024-00732-2

https://rdcu.be/d2UPp

https://github.com/MinglangYin/DIMON},

doi = {10.1038/s43588-024-00732-2},

year  = {2024},

date = {2024-02-13},

urldate = {2024-02-13},

journal = {Nature Computational Science},

abstract = {The solution of a PDE over varying initial/boundary conditions on multiple domains is needed in a wide variety of applications, but it is computationally expensive if the solution is computed de novo whenever the initial/boundary conditions of the domain change. We introduce a general operator learning framework, called DIffeomorphic Mapping Operator learNing (DIMON) to learn approximate PDE solutions over a family of domains ${Omega_{theta}}_theta$, that learns the map from initial/boundary conditions and domain $Omega_theta$ to the solution of the PDE, or to specified functionals thereof. DIMON is based on transporting a given problem (initial/boundary conditions and domain $Omega_theta$) to a problem on a reference domain $Omega_0$, where training data from multiple problems is used to learn the map to the solution on $Omega_0$, which is then re-mapped to the original domain $Omega_theta$. We consider several problems to demonstrate the performance of the framework in learning both static and time-dependent PDEs on non-rigid geometries; these include solving the Laplace equation, reaction-diffusion equations, and a multiscale PDE that characterizes the electrical propagation on the left ventricle. This work paves the way toward the fast prediction of PDE solutions on a family of domains and the application of neural operators in engineering and precision medicine.},

keywords = {cardiac electrophysiology, digital twins, Machine learning, model reduction, neural networks, PDEs, precision medicine},

pubstate = {published},

tppubtype = {article}

}