Research team

Research staff


Patrick Blonigan

Senior Member of Technical Staff

Sensitivity analysis, Model reduction, Chaotic dynamical systems, Computational fluid dynamics


Brian Freno

Senior Member of Technical Staff

Model reduction, Machine learning, Error modeling, Finite element analysis


Chi Hoang

Senior Computer Science R&D Researcher

Model reduction, Reduced basis method, Domain decomposition, Finite element analysis


Francesco Rizzi

Senior Computational Scientist

High performacne computing, Uncertainty quantification, Model reduction, Numerical methods, Object-oriented programming


John Tencer

Principal Member of Technical Staff

Radiation heat transfer, Uncertainty quantification, Model reduction, Finite element analysis

Postdoctoral researchers


Kookjin Lee

Postdoctoral researcher

Deep learning, Autoencoders, Model reduction, Uncertainty quantification, Sparse linear solvers


Eric Parish

John von Neumann Postdoctoral Fellow

Model reduction, Closure modeling, Machine learning, Dynamics learning


Yukiko Shimizu

Postdoctoral researcher

Model reduction, Sensitivity analysis, Chaotic dynamical systems, Numerical methods

Graduate student interns and collaborators


Ricardo Baptista

PhD Candidate, MIT

Uncertainty quantification, High-dimensional statistics, Machine learning


Philip Etter

PhD Candidate, Stanford University

Model reduction, Adaptive refinement, Machine learning, Deep learning


Sofia Guzzetti

PhD candidate, Emory University

Uncertainty quantification, Domain decomposition, Hemodynamics


Moving to Facebook Research

Data-driven coarse propagation to accelerate convergence

Using regression to model approximate-solution errors

Becoming Associate Editor

Using deep learning to overcome Komolgorov-width limitation

Recent Publications

This work proposes a machine-learning framework for modeling the error incurred by approximate solutions to parameterized dynamical …

This work introduces a new method to efficiently solve optimization problems constrained by partial differential equations (PDEs) with …

Data I/O poses a significant bottleneck in large-scale CFD simulations; thus, practitioners would like to significantly reduce the …

This work proposes a data-driven method for enabling the efficient, stable time-parallel numerical solution of systems of ordinary …

This work proposes a machine-learning framework for constructing statistical models of errors incurred by approximate solutions to …



Teaching assistant


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