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[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] |

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[3] | Multifidelity Cross-Entropy Estimation of Conditional Value-at-Risk for Risk-Averse Design Optimization. In AIAA Scitech 2020 Forum, AIAA, 2020. [ Abstract] [BibTeX] |

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[8] | Density Estimation with Adaptive Sparse Grids for Large Data Sets. In SIAM Data Mining 2014, SIAM, 2014. [ Abstract] [BibTeX] |

[9] | Classification with Probability Density Estimation on Sparse Grids. In Sparse Grids and Applications 2012, Volume 97 of Lecture Notes in Computational Science and Engineering, 2014. (accepted). [ BibTeX] |

[10] | Image Segmentation with Adaptive Sparse Grids. In AI 2013: Advances in Artificial Intelligence, Volume 8272 of Lecture Notes in Computer Science Volume, pages 160-165, Springer, 2013. [ BibTeX] |

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[1] | Model Order Reduction of Parametrized Systems with Sparse Grid Learning Techniques. Technische Universität München, Munich, Germany, 2013. [ BibTeX] |

[1] | Scientific Machine Learning with Operator Inference and Re-Projection. In SIAM Conference on Computational Science and Engineering 2021 SIAM, online, 2021. |

[2] | Nonlinear model reduction for transport-dominated problems. In Applied Mathematics Seminar, Courant Institute, online, 2021. |

[3] | Scientific machine learning with operator inference and re-projection. In Aerospace Computational Design Laboratory Seminar, Massachusetts Institute of Technology, Cambridge, MA, 2020. |

[4] | Nonlinear model reduction for transport-dominated problems. In Numerical Analysis and PDE Seminar, University of Delaware, Newark, DE, 2020. |

[5] | A biased introduction to projection-based model reduction. In Descriptors of Energy Landscapes Using Topological Data Analysis Seminar Series, online, 2020. |

[6] | Quasi-optimal sampling to learn basis updates for online adaptive model reduction with adaptive empirical interpolation. In American Control Conference (ACC) 2020, Denver, CO, 2020. |

[7] | Learning low-dimensional dynamical-system models from data via non-intrusive model reduction. In MAC-MIGS afternoon on Randomness and Data, Edinburgh, United Kingdom, 2020. |

[8] | Context-aware learning of surrogate models for multi-fidelity computations. In Computational Uncertainty Quantification: Mathematical Foundations, Methodology & Data, Vienna, Austria, 2020. |

[9] | Sampling low-dimensional Markovian dynamics for learning certified reduced models from data. In Mathematics of Reduced Order Models, Institute for Computational and Experimental Research in Mathematics, Providence, RI, 2020. |

[10] | Learning dynamical-system models from data with time-domain Loewner, dynamic mode decomposition, and operator inference. In Model and dimension reduction in uncertain and dynamic systems, Institute for Computational and Experimental Research in Mathematics, Providence, RI, 2020. |

[11] | Recovering certified reduced dynamical-system models from data with operator inference. In SIAM Minisymposium on Applications of Machine Learning to the Analysis of Nonlinear Dynamical Systems at Joint Mathematics Meetings 2020, Denver, CO, 2020. |

[12] | Multifidelity Cross-Entropy Estimation of Conditional Value-at-Risk for Risk-Averse Design Optimization. In AIAA SciTech 2020, Orlando, FL, 2020. |

[13] | Recovering (certified) reduced models from data with operator inference and time-domain Loewner. In European Numerical Mathematics and Advanced Applications Conference, Amsterdam, Netherlands, 2019. |

[14] | Sampling Markovian dynamics for learning low-dimensional dynamical-system models from data. In Computational Science Initiative, Brookhaven National Laboratory, Brookhaven, NY, 2019. |

[15] | Sampling Markovian dynamics for learning low-dimensional dynamical-system models from data. In Workshop on Uncertainty Quantification, Machine Learning & Bayesian Statistics in Scientific Computing, University of Heidelberg, Heidelberg, Germany, 2019. |

[16] | Data generation and time-delay corrections for learning reduced models with operator inference. In Physics Informed Machine Learning Workshop, Seattle, WA, 2019. |

[17] | Learning reduced dynamical-system models from data via operator inference and Loewner interpolation. In Oden Institute for Computational Engineering and Sciences (ICES), University of Texas at Austin, Austin, TX, 2019. |

[18] | Dynamic Coupling of Full and Reduced Models via Randomized Online Basis Updates. In SIAM Computational Science and Engineering 2019, Spokane, WA, 2019. |

[19] | Model reduction for transport-dominated problems via adaptive basis updates. In Applied Mathematics Colloquium, Columbia, New York, NY, 2018. |

[20] | Learning Context-Aware Reduced Models for Multifidelity Computations. In School for Simulation and Data Sciences, RWTH Aachen, Aachen, Germany, 2018. |

[21] | Context-Aware Model Reduction. In Computational Mathematics and Simulation Science Seminar, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, 2018. |

[22] | Dynamic Coupling of Full and Reduced Models via Randomized Online Basis Updates. In Numerical Analysis and Scientific Computing Seminar, Courant Institute, New York, NY, 2018. |

[23] | Data-Driven Multifidelity Methods for Monte Carlo Estimation. In Workshop on Big Data Meets Large-Scale Computing, Institute for Pure & Applied Mathematics (IPAM), Los Angeles, CA, 2018. |

[24] | Learning context-aware surrogate models for multifidelity uncertainty quantification. In World Congress in Computational Mechanics, New York, NY, 2018. |

[25] | Multifidelity methods and context-aware model reduction for Monte Carlo estimation and beyond. In Seminar Numerische Mathematik, Technical University Berlin, Berlin, Germany, 2018. |

[26] | A Multifidelity Cross-Entropy Method for Rare Event Simulation. In SIAM Uncertainty Quantification 2018, Garden Grove, CA, 2018. |

[27] | Data-Driven Multifidelity Methods for Monte Carlo Estimation. In Model Reduction of Parametrized Systems (MoRePaS) IV, Nantes, France, 2018. |

[28] | Multifidelity Monte Carlo estimation with adaptive low-fidelity models. In Reducing dimensions and cost for UQ in complex systems, Isaac Newton Institute for Mathematical Sciences, Cambridge, UK, 2018. |

[29] | Data-Driven Multifidelity Methods for Monte Carlo Estimation. In Engineering Physics Seminars and Colloquium, University of Wisconsin-Madison, Madison, USA, 2018. |

[30] | Multifidelity Monte Carlo estimation for large-scale uncertainty propagation. In 2018 AIAA Non-Deterministic Approaches Conference (AIAA SciTech), Kissimmee, USA, 2018. |

[31] | Multifidelity methods for rare event simulation. In European Numerical Mathematics and Advanced Applications (ENUMATH), Bergen, Norway, 2017. |

[32] | Online adaptive discrete empirical interpolation for nonlinear model reduction. In European Numerical Mathematics and Advanced Applications (ENUMATH), Bergen, Norway, 2017. |

[33] | Multifidelity methods for uncertainty propagation and rare event simulation. In QUIET 2017 - Quantification of Uncertainty: Improving Efficiency and Technology SISSA, Trieste, Italy, 2017. |

[34] | Optimal low-rank updates for online adaptive model reduction with the discrete empirical interpolation method. In Householder Symposium XX on Numerical Linear Algebra, Blacksburg, USA, 2017. |

[35] | Multifidelity Monte Carlo Methods for Rare Event Simulation. In MATRIX Workshop on Inverse Problems, Melbourne, Australia, 2017. |

[36] | Data-driven reduced model construction with the time-domain Loewner framework and operator inference. In Colloquium, Department of Mathematics, Virginia Tech, Blacksburg, USA, 2017. |

[37] | Multifidelity Methods for Uncertainty Propagation and Rare Event Simulation. In Workshop on Data-Driven Modeling and Uncertainty Quantification (UQPM), Austin, USA, 2017. |

[38] | Multifidelity Monte Carlo Methods with Optimally-Adapted Surrogate Models. In SIAM Computational Science and Engineering 2017, Atlanta, USA, 2017. |

[39] | Optimal sampling in multifidelity Monte Carlo estimation for efficient uncertainty propagation. In SILO Seminar Wisconsin Institute for Discovery, Madison, USA, 2017. |

[40] | Optimal sampling in multifidelity Monte Carlo estimation for efficient uncertainty propagation. In Applied and Computational Mathematics Seminar Department of Mathematics, University of Wisconsin-Madison, Madison, USA, 2016. |

[41] | Safe and Efficient Data-Driven Model Reduction for Critical Engineering Applications. In Next Generation Mobility Modeling and Simulation, Novi, USA, 2016. |

[42] | Data-Driven Methods for Nonintrusive Model Reduction. In SIAM Annual Meeting 2016, Boston, USA, 2016. |

[43] | Multifidelity Methods for Uncertainty Quantification. In Workshop on Data to Decisions in Aerospace Engineering, Auckland, New Zealand, 2016. |

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[1] | Learning Data-Fit Models for Multi-Fidelity Monte Carlo Estimation of Energetic Particle Loss in Fusion Reactors. In SIAM Conference on Computational Science and Engineering 2021, online, 2021. |

[2] | Reduced Deep Networks: Distilling Nonlinear Shock Waves. In SIAM Conference on Computational Science and Engineering 2021, online, 2021. |

[3] | Trading-off Deterministic Preconditioning and Sampling in Bayesian Inference. In SIAM Conference on Computational Science and Engineering 2021, online, 2021. |

[4] | Learning Context-Aware Surrogate Models for Multifidelity Importance Sampling and Bayesian Inverse Problems. In SIAM Conference on Computational Science and Engineering 2021, online, 2021. |

[5] | Certified Predictions from Data: A Posteriori Error Estimators for Non-Intrusive Reduced Models Learned from Data. In SIAM Conference on Computational Science and Engineering 2021, online, 2021. |

[6] | Learning Locally Stable Low-Dimensional Dynamical-System Models with Quadratic Nonlinear Terms from State Trajectories with Operator Inference. In SIAM Conference on Computational Science and Engineering 2021, online, 2021. (poster). |

[7] | Operator inference: A posteriori error estimation for reduced models learned from data and non-Markovian correction terms. In Numerical Analysis and Scientific Computing seminar, Courant Institute, New York University, online, 2020. |

[8] | Low-rank transport for 2D waves: A dimensional splitting approach. In SIAM Conference on Mathematics of Data Science (MDS20), online, 2020. |

[9] | Manifold Approximations via Transported Subspaces: Model reduction for transport-dominated problems. In Mathematics Colloquium, University of Central Florida, Orlando, FL, 2020. |

[10] | Manifold Approximation via Transported Subspaces (MATS). In Computational Mathematics Seminar, University of Pittsburgh, Pittsburgh, PA, 2019. |

[11] | Learning context-aware surrogate models for multifidelity importance sampling and Bayesian inverse problems. In Computational Statistics and Data-Driven Modeling, Institute for Computational and Experimental Research in Mathematics, Providence, RI, 2020. |

[12] | Learning context-aware surrogate models for multifidelity importance sampling and Bayesian inverse problems. In Mathematics of Reduced Order Models, Institute for Computational and Experimental Research in Mathematics, Providence, RI, 2020. |

[13] | Manifold Approximations via Transported Subspaces. In Mathematics of Reduced Order Models, Institute for Computational and Experimental Research in Mathematics, Providence, RI, 2020. |

[14] | Pre-asymptotic error estimation for reduced dynamical-system models learned from data. In Optimization and Inversion under Uncertainty, Johann Radon Institute, Linz, Austria, 2019. |

[15] | Model Reduction of Nonlinear Hyperbolic Problems Using Low-dimensional Transport Modes. In European Numerical Mathematics and Advanced Applications Conference, Amsterdam, Netherlands, 2019. |

[16] | Data generation and stabilization for reduced modeling with operator inference. In SIAM Computational Science and Engineering 2019, Spokane, WA, 2019. |