People

Faculty

Andrew Stuart
Bren Professor of Computing and Mathematical Sciences

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.

Staff

Postdoctoral Scholars

Oliver Dunbar
Postdoctoral Scholar in Environmental Science and Engineering

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.

Michelle Feng
Postdoctoral Scholar in Computing and Mathematical Sciences

Michelle Feng is a postdoctoral researcher working on developing topological tools for analyzing complex social systems, especially social systems that are informed by spatial patterns (e.g. housing, voting). Her other research interests include studying network structure (especially higher order structures in networks), and the intersection of topology and machine learning. Outside of mathematics, she is interested in advocacy, literature, and crafting.

Daniel Huang
Postdoctoral Scholar in Environmental Science and Engineering

My research interest lies in advancing fundamental understanding and predictive modeling for real world engineering applications and important natural phenomena. Previously, I focus on developing mathematical models and advanced computational algorithms (e.g. embedded boundary method and high order methods). Recently, I start to explore data-driven approaches to improve these models and quantify uncertainties (e.g. neural networks and Bayesian inversion).

Anshuman Pradhan
Postdoctoral Scholar in Computing and Mathematical Sciences

Anshuman’s research interests focus on developing geophysical data informed computational modeling methods to aid in sustainable development of earth resources. He employs methods from Bayesian statistics, machine learning, signal processing and geostatistics to address research challenges in geoscientific applications such as groundwater development and management, hydrocarbon reservoir characterization, seismic imaging and inversion.

Elizabeth Qian
Von Karman Instructor in Computing and Mathematical Sciences

My research interests are motivated by the need for scalable computational methods to enable and enhance decision-making in engineering, scientific, and medical applications. In particular, my work focuses on the development of principled low-dimensional model approximations through model reduction and scientific machine learning, and on designing multi-fidelity formulations for optimization and uncertainty quantification that embed these approximations in decision-making settings.

Jinlong Wu
Postdoctoral Scholar in Environmental Science and Engineering

My previous research mainly focuses on data-driven turbulence modeling by using Bayesian inference and machine learning techniques. More recently, I also started to explore generative learning techniques (e.g. generative adversarial networks) to emulate and predict PDE-governed systems. In general, my research interests lie in an interdisciplinary area of computational physics, applied mathematics and statistics.

Graduate Students

Dmitry Burov
Graduate Student in Applied and Computational Mathematics

I come from a numerical PDEs background, having worked on finite difference, finite element and pseudo-spectral methods for Schroedinger and Navier--Stokes equations. My current research is developing data assimilation tools for global climate models, in particular, the focus is on experimental design questions and ODE/PDE averaging. I am also interested in Koopman analysis, diffusion maps, and parameter estimation.

Edoardo Calvello
Graduate Student in Applied and Computational Mathematics

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.

Yifan Chen
Graduate Student in Applied and Computational Mathematics

I am broadly interested in theoretical and computational math problems arising at the intersection of physics, computer, and information science. Specifically, I’ve been doing research in multiscale analysis, numerical analysis, machine learning and statistics.

Haakon Ludvig Langeland Ervik
Graduate Student in Environmental Science and Engineering

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.

Nikola Kovachki
Graduate Student in Applied and Computational Mathematics

My research interests lie at the intersection of learning theory and inverse problems. I am keen on the development of the mathematical theory of learning and its implications for advancement of numerical algorithms. Further I am interested in the application of existing learning systems to physical problems arising in the sciences.

Matthew Levine
Graduate Student in Computing and Mathematical Sciences

I am interested in developing novel methods within the intersections of dynamical systems, machine learning, and data assimilation, and have most often applied these methods to biomedical contexts, including modeling and prediction of the glucose-insulin system.

Nicholas Nelsen
Graduate Student in Mechanical Engineering

I have research interests in theory and algorithms for high-dimensional scientific and data-driven computation. My current work is centered on operator regression, with application to efficient surrogates for forward and inverse problems arising from models of physical systems. To this end, I develop and utilize tools from machine learning, model reduction, and numerical/statistical analysis.

Margaret Trautner
Graduate Student in Computing and Mathematical Sciences

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.