Professor Stuart's research is focused on the development of foundational mathematical and algorithmic frameworks for the seamless integration of models with data. He works in the Bayesian formulation of inverse problems for differential equations, and in data assimilation for dynamical systems.
My research interests include partial differential equations and fluid dynamics, with practical emphases in data assimilation, optimization, high performance computing, and numerical analysis. I find the intersection of theoretical and applied mathematics to be more fruitful than studying either subject in isolation.
My research addresses issues at the heart of computational engineering, sciences, and medicine: to understand, enhance, and control the quality, validity, and reliability of simulation-based predictions of complex physical systems. I have worked on various uncertainty quantification and optimization problems associated with models governed by parametric partial differential equations. I have extensively worked on application problems associated with computational polymer science. I am currently working on constitutive modeling for solid mechanics.
My research interests include data assimilation, stochastic process models, graph-based machine learning and active learning. My previous researches applied graph-based active learning to image classification and segmentation problems, while also extending methods by developing novel batch active learning approaches. Earlier this year, I also explored the integration of large language models with knowledge graphs. Currently, my primary research focus is on data assimilation.
I am interested in the development of efficient methods for operator approximation and their application to Bayesian data assimilation for fluid flows. Part of my research focuses on emerging operator learning architectures, aiming to contribute to the theoretical underpinnings of these methods, and seeking to improve their efficiency and reliability. My other research focuses on fluid flows, their ill-posedness and implications for their reliable approximation by numerical methods.
My research develops statistical and machine learning methods to model spatial and time varying processes from data; specifically in physical, biological and engineering problems where nonlocal effects are present. Examples include inference of Green’s functions, autoregressive or memory kernel models, and fractional order or higher-order equations. I am also interested in finding compressed interpretable representations of partially observed systems, which often introduce nonlocal interactions in the observed coordinates as well as in time. My prior work applied optimal transport algorithms to trajectory inference and biological matching, and I am now exploring how dynamical systems can be compared using optimal transport metrics.
I am primarily interested in developing efficient Bayesian inference techniques for probabilistic models such as Gaussian processes and stochastic (partial) differential equations, with the goal of making them applicable in real-world applications. Methods I have considered include variational inference, inclusion of physical or geometric/topological inductive biases, and deep learning methodologies. Prior to this, I have also done research at the intersection of geometric and statistical mechanics, as well as analysis of stochastic fluid PDEs.
My research interests are in numerical algorithms for PDEs, mean field games, and gaussian processes.
My work lies at the intersection of data assimilation, stochastic analysis, dynamical systems, computational statistics and machine learning. In general, I am interested in using theoretical insights from analysis to develop novel computationally efficient numerical algorithms.
My research delves into the area of high-dimensional chaotic dynamical systems. I am particularly interested in extreme events, data assimilation, uncertainty quantification, and the application of machine learning to uncover constitutive laws. Currently, my focus is on studying the predictability of earthquakes and Slow Slip Events (SSEs). In the intersection of mathematics, geophysics, and data science, my work aims to shed light on complex, high-dimensional systems that have the potential to improve our understanding and ability to predict natural phenomena.
Ervik’s research interests broadly encompass stochastic models for the climate. He has a background in applied mathematics and is currently working on developing stochastic closures for subgrid-scale cloud models. The goal of this research is to develop a model that more faithfully incorporates uncertainty. As part of this, he also works on methods for robust parameter estimation and uncertainty quantification.
I am interested in problems at the intersection of mechanics, data-driven modelling, statistics and machine learning. Currently, I am working on multiscale modelling problems and learning thermodynamically stable material constitutive laws from data.
My research interests lie at the intersection of computational science and applied mathematics. I work on problems involving data assimilation to improve models of dynamical systems.
My research interests lie at the intersection of computer science and applied mathematics. I work on data-driven methods for computational problems in natural science.
Research Interests in Applied analysis and simulation: Partial differential equations, optimization, deterministic modelling, inverse problems, numerical analysis and implementation; Uncertainty quantification and probabilistic techniques: Bayesian inverse problems, optimal design, scalability of algorithms, machine learning.
My research is on developing scalable computational methods for uncertainty quantification in complex physical systems. In particular, my work leverages measure transport and dimension reduction to design principled techniques for probabilistic modeling and Bayesian inference. Broadly, I am motivated by using these algorithms to improve predictions and gain insights on systems in engineering, geophysics, and medicine.
