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


Data-driven coarse propagation to accelerate convergence

Excited to welcome Yuki to our lab

Using regression to model approximate-solution errors

Becoming Associate Editor

in the areas of reduced-order modeling, scientific machine learning, high-performance computing, and uncertainty quantification

Recent Publications

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 …

In many applications, projection-based reduced-order models (ROMs) have demonstrated the ability to provide rapid approximate solutions …

This work proposes a technique for constructing a statistical closure model for reduced-order models (ROMs) applied to stationary …



Teaching assistant


  • 925 667 1834
  • 7011 East Avenue, MS 9159, Livermore, California, 94550, USA