Department of Computer Science | Department of Mathematics (affiliated) | Center for Data Science (affiliated)

Courant Institute of Mathematical Sciences | New York University

251 Mercer Street, New York, NY 10012

computational mathematics, machine learning, computational statistics, numerical analysis, and scientific computing

Co-organizer, with Themistoklis Sapsis (MIT), of minisymposium "Learning Dynamics from Data for Prediction and Control" at the SIAM Conference on Computational Science And Engineering 2021.

I feel very honored to be an NSF CAREER recipient. My proposal Formulations, Theory, and Algorithms for Nonlinear Model Reduction in Transport-Dominated Systems was funded through the Computational Mathematics program in the Division Of Mathematical Sciences.

Excited to be a 2021 AFRL/AFOSR Young Investigator Program (YIP) award recipient. My proposal is about *context-aware learning* for intelligent decision-making in science and engineering.

Looking forward to Mathematical and Scientific Machine Learning (MSML) 2021 and happy to serve on the program committee.

I am co-organizing the Numerical Analysis and Scientific Computing seminar at Courant Institute starting fall 2020.

Invited to present our work on multifidelity methods as a keynote lecture in the minisymposium Multifidelity Optimization at the European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS).

Presenting at summer school Learning Models from Data:
Model Reduction, System Identification and Machine Learning at Max Planck Institute for Dynamics of Complex Technical Systems, organized by GAMM Juniors.

Invited to give a tutorial presentation on multifidelity methods for uncertainty quantification at the workshop Multifidelity Modeling in Support of Design and Uncertainty Quantification that is organized as part of the AIAA Aviation Forum.

Co-organizer, with Themistoklis Sapsis (MIT), of minisymposium "Learning Dynamical-System Models for Prediction and Control" at the SIAM Conference on Mathematics of Data Science 2020.

Congratulations to Elizabeth Qian for winning a 2020 SIAM Student Paper Prize for her work Multifidelity Monte Carlo Estimation of Variance and Sensitivity Indices. The paper appeared in SIAM/ASA Journal on Uncertainty Quantification in 2018.

Congratulations to Frederick Law for winning a DoD NDSEG Fellowship.

Co-organizer of ICERM workshop on Computational Statistics and Data-Driven Models, which will be held virtually April 20-24, 2020.

Co-organizer with Youssef Marzouk (MIT) and Yaoliang Yu (U Waterloo) of minisymposium on "Inference and preconditioning via Stein methods, flows, and other transport maps" at the SIAM Conference on Uncertainty Quantification 2020.

Invited to give a presentation on model reduction for transport-dominated problems at workshop on "Mathematics of Reduced Order Models", which is organized at the Institute for Computational and Experimental Research in Mathematics (ICERM).

Invited to give a tutorial presentation on learning dynamical-system models from data as part of the program "Model and dimension reduction in uncertain and dynamic systems" at the Institute for Computational and Experimental Research in Mathematics (ICERM).

Invited to give a presentation in the seminar of the Computational Science Initiative at Brookhaven National Laboratory.

Invited to give a presentation at the Workshop on Uncertainty Quantification, Machine Learning & Bayesian Statistics in Scientific Computing at University of Heidelberg (Germany).

Invited to give a presentation at the Physics Informed Machine Learning Workshop at University of Washington.

Invited to give a tutorial presentation for aerospace engineers on our multifidelity uncertainty quantification work at the AIAA Aviation workshop on Multifidelity Modeling In Support Of Design And Uncertainty Quantification.

There currently are 2 articles that I co-authored under the top-20 downloads of the SIAM/ASA Journal on Uncertainty Quantification (March 30, 2019). The articles are on multifidelity sensitivity analysis (authors Qian, P., O'Malley, Vesselinov, and Willcox) and on multifidelity rare event simulation (authors P., Kramer, and Willcox).

Invited to give a presentation in the seminar series of the Institute for Computational Engineering and Sciences (ICES) at University of Texas, Austin.

SIAM selected our minisymposium "Model Reduction for Problems with Strong Convection, Sharp Gradients, and Discontinuities" (co-organized with Maciej Balajewicz and Gerrit Welper) to be a Featured Minisymposium at the SIAM Conference on Computational Science and Engineering 2019.

Invited to give a presentation in the Applied Mathematics Colloquium at Columbia University.

Invited to give a presentation in the SSD Seminar Series at the Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen.

Invited to give a presentation in the seminar of the Mathematics in Computational Science and Engineering group at EPFL.

I will join Courant Institute of Mathematical Sciences at New York University as assistant professor on September 1, 2018.

Invited to speak in the program "Science at Extreme Scales: Where Big Data Meets Large-Scale Computing" at the Institute for Pure & Applied Mathematics (IPAM).

I have been selected for a Department of Energy (DoE) Early Career Award in the Applied Mathematics Program (Office of Advanced Scientific Computing Research). My move to Courant Institute (New York University) delayed the process to Fall 2018 and so my grant is not part of the official announcement posted in Summer 2018.

I have been invited to participate as a Research Fellow in the program Model and dimension reduction in uncertain and dynamic systems at the Institute for Computational and Experimental Research in Mathematics (ICERM) at Brown University in Spring 2020.

Co-organizer of minisymposium on multilevel and multifidelity methods for Bayesian inverse problems at SIAM Uncertainty Quantification 2018; with Tiangang Cui (Monash University).

Invited speaker at the Model Reduction of Parametrized Systems (MoRePaS) IV conference.

Invited to speak at the workshop "Reducing dimensions and cost for UQ in complex systems", which is held at the Isaac Newton Institute for Mathematical Sciences.

Our survey paper on multifidelity methods for outer-loop applications has been accepted by SIAM Review.

Our work on data-driven nonintrusive model reduction with operator inference is cited in SIAM News.

Invited to speak at the workshop Quantification of Uncertainty: Improving Efficiency and Technology that is organized by Marta D'Elia (Sandia), Max Gunzburger (Florida State), and Gianluigi Rozza (SISSA).

Presentation in the colloquium of the Department of Mathematics at Virginia Tech.

Invited presentation at the workshop Uncertainty Quantification and Data-Driven Modeling that is organized by James R. Stewart (Sandia) and Krishna Garikipati (UMich).

Co-organizer of minisymposium on surrogate modeling at SIAM Computational Science and Engineering 2017; with Gianluigi Rozza (SISSA).

Presentation in the Applied and Computational Mathematics Seminar at University of Wisconsin-Madison.

I started as Assistant Professor at University of Wisconsin-Madison.

Invited presentation at workshop on Next Generation Mobility Modeling and Simulation, UW-Madison.

Co-organizer of minisymposium on model reduction at SIAM Annual Meeting 2016.

Co-organizer of the workshop on data-driven model reduction and machine learning.

Invited talk in the seminar series of the Transregional Collaborative Research Center on Invasive Computing.

Co-organizer of minisymposium on adaptive model reduction at SIAM CSE 15.

I was awarded the Heinz Schwärtzel prize for my PhD thesis.

Invited talk in the scientific computing colloquium at TUM.

Co-organizer of minisymposium on density estimation at SIAM UQ 14.

[1] | Multilevel Stein variational gradient descent with applications to Bayesian inverse problems. arXiv:2104.01945, 2021. [ Abstract] [BibTeX] |

[2] | Data-driven low-fidelity models for multi-fidelity Monte Carlo sampling in plasma micro-turbulence analysis. arXiv:2103.07539, 2021. [ Abstract] [BibTeX] |

[3] | Operator inference of non-Markovian terms for learning reduced models from partially observed state trajectories. arXiv:2103.01362, 2021. [ Abstract] [BibTeX] |

[4] | Context-aware surrogate modeling for balancing approximation and sampling costs in multi-fidelity importance sampling and Bayesian inverse problems. arXiv:2010.11708, 2020. [ Abstract] [BibTeX] |

[5] | Depth separation for reduced deep networks in nonlinear model reduction: Distilling shock waves in nonlinear hyperbolic problems. arXiv:2007.13977, 2020. [ Abstract] [BibTeX] |

[6] | Manifold approximations via transported subspaces: Model reduction for transport-dominated problems. arXiv:1912.13024, 2019. [ Abstract] [BibTeX] |