[1] | Sequential-in-time training of nonlinear parametrizations for solving time-dependent partial differential equations. arXiv, 2404.01145, 2024. [ Abstract] [BibTeX] |

[2] | Greedy construction of quadratic manifolds for nonlinear dimensionality reduction and nonlinear model reduction. arXiv, 2403.06732, 2024. [ Abstract] [BibTeX] |

[3] | Nonlinear embeddings for conserving Hamiltonians and other quantities with Neural Galerkin schemes. arXiv, 2310.07485, 2023. [ Abstract] [BibTeX] |

[4] | Multifidelity Covariance Estimation via Regression on the Manifold of Symmetric Positive Definite Matrices. arXiv, 2307.12438, 2023. [ Abstract] [BibTeX] |

[1] | CoLoRA: Continuous low-rank adaptation for reduced implicit neural modeling of parameterized partial differential equations. International Conference on Machine Learning (ICML), 2024. [ Abstract] [BibTeX] |

[2] | Randomized Sparse Neural Galerkin Schemes for Solving Evolution Equations with Deep Networks. NeurIPS 2023 (spotlight). [ Abstract] [BibTeX] |

[3] | Multi-fidelity covariance estimation in the log-Euclidean geometry. International Conference on Machine Learning (ICML), 2023. [ Abstract] [BibTeX] |

[4] | Reduced models with nonlinear approximations of latent dynamics for model premixed flame problems. Proceedings of Workshop on Reduced order models; Approximation theory; Machine learning; Surrogates, Emulators and Simulators (RAMSES), 2023. [ Abstract] [BibTeX] |

[5] | Towards context-aware learning for control: Balancing stability and model-learning error. In IEEE American Control Conference, 2022. [ Abstract] [BibTeX] |

[6] | Multilevel Stein variational gradient descent with applications to Bayesian inverse problems. In Mathematical and Scientific Machine Learning (MSML) 2021, 2021. [ Abstract] [BibTeX] |

[7] | An Extensible Benchmark Suite for Learning to Simulate Physical Systems. In NeurIPS 2021 Track Datasets and Benchmarks, 2021. (accepted). [ Abstract] [BibTeX] |

[8] | Learning low-dimensional dynamical-system models from noisy frequency-response data with Loewner rational interpolation. In Realization and Model Reduction of Dynamical Systems: A Festschrift in Honor of the 70th Birthday of Thanos Antoulas, Springer, 2020. [ Abstract] [BibTeX] |

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

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

[11] | Multifidelity Monte Carlo estimation for large-scale uncertainty propagation. In 2018 AIAA Non-Deterministic Approaches Conference, AIAA, 2018. [ Abstract] [BibTeX] |

[12] | Optimal Approximations of Coupling in Multidisciplinary Models. In 58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, AIAA, 2017. [ BibTeX] |

[13] | Detecting and Adapting to Parameter Changes for Reduced Models of Dynamic Data-driven Application Systems . In International Conference on Computational Science, Volume 51 of Procedia Computer Science, pages 2553-2562, Elsevier, 2015. [ BibTeX] |

[14] | Parametric model order reduction by sparse-grid-based interpolation on matrix manifolds for multidimensional parameter spaces. In European Control Conference (ECC) 2014, IEEE, 2014. [ BibTeX] |

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

[16] | 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] |

[17] | 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] |

[18] | Analysis of car crash simulation data with nonlinear machine learning methods. In International Conference on Computational Science, Volume 18 of Procedia Computer Science, pages 621-630, Elsevier, 2013. [ BibTeX] |

[19] | Model Reduction with the Reduced Basis Method and Sparse Grids. In Sparse Grids and Applications 2011, Volume 88 of Lecture Notes in Computational Science and Engineering, pages 223-242, Springer, 2013. [ BibTeX] |

[20] | Fast Insight into High-Dimensional Parametrized Simulation Data. In 11th International Conference on Machine Learning and Applications (ICMLA), pages 265-270, IEEE, 2012. [ BibTeX] |

[21] | Clustering Based on Density Estimation with Sparse Grids. In KI 2012: Advances in Artificial Intelligence, Volume 7526 of Lecture Notes in Computer Science, pages 131-142, Springer, 2012. [ BibTeX] |

[22] | Sparse Grid Classifiers as Base Learners for AdaBoost. In International Conference on High Performance Computing and Simulation (HPCS), pages 161-166, IEEE, 2012. [ BibTeX] |

[23] | Semi-Coarsening in Space and Time for the Hierarchical Transformation Multigrid Method. In International Conference on Computational Science, Volume 9 of Procedia Computer Science, pages 2000-2003, Elsevier, 2012. [ BibTeX] |

[24] | A Sparse-Grid-Based Out-of-Sample Extension for Dimensionality Reduction and Clustering with Laplacian Eigenmaps. In AI 2011: Advances in Artificial Intelligence, Volume 7106 of Lecture Notes in Computer Science, pages 112-121, Springer, 2011. [ BibTeX] |

[1] | Neural Galerkin schemes for sequential-in-time solving of partial differential equations with deep networks. In Handbook of Numerical Analysis: Numerical Analysis Meets Machine Learning, pages 1-30, Elsevier, 2024. [ Abstract] [BibTeX] |

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

[1] | Leveraging nonlinear latent dynamics for data-driven predictions. In Mathematical Sciences seminar, IBM, New York, NY, 2024. |

[2] | Neural Galerkin schemes for model reduction of transport-dominated problems. In Numerical Analysis of Galerkin ROMs online seminar series, online, 2024. |

[3] | Leveraging nonlinear latent dynamics for data-driven predictions. In Widely Applied Mathematics Seminar, Cambridge, MA, 2024. |

[4] | Leveraging nonlinear latent dynamics for data-driven predictions. In Center for Approximation and Mathematical Data Analytics, College Town, TX, 2024. |

[5] | Randomized sparse Neural Galerkin schemes for solving evolution equations with deep networks. In MORTech - International Workshop on Model Reduction Techniques, Paris, France, 2023. |

[6] | Nonlinear model reduction with adaptive bases and adaptive sampling. In Applied Mathematics and Scientific Computing Seminar, Temple University, Philadelphia, PA, 2023. |

[7] | Nonlinear model reduction with adaptive bases and adaptive sampling. In International Council for Industrial and Applied Mathematics (ICIAM) Congress, Tokyo, Japan, 2023. (online). |

[8] | Nonlinear parametrizations for mitigating the Kolmogorov barrier in model reduction. In International Conference on Spectral and High Order Methods (ICOSAHOM), Seoul, South Korea, 2023. |

[9] | Adaptivity in Reduced Order models. In Data-driven and Reduced Order Modeling for Multi-Scale Problems, Dayton, OH, 2023. |

[10] | Context-aware controller inference. In Workshop on The mathematical and statistical foundation of future data-driven engineering, Cambridge, UK, 2023. |

[11] | Coupling adaptive sampling and training with Neural Galerkin schemes for high-dimensional evolution equations. In Workshop on Scientific Machine Learning, Austin, TX, 2023. |

[12] | Active sampling and Neural Galerkin schemes for high-dimensional evolution equations. In Numerical Analysis Seminar, University of Hong Kong, Hong Kong, Hong Kong, 2023. |

[13] | Neural Galerkin Schemes for Evolution Equations. In SIAM Conference on Computational Science and Engineering, Amsterdam, Netherlands, 2023. |

[14] | Multi-Fidelity Methods. In Simons Hour Talks, Simons Collaboration on Hidden Symmetries and Fusion Energy, Princeton, NJ, 2023. |

[15] | Scientific machine learning with multi-fidelity methods and operator inference. In Siemens, Princeton, NJ, 2023. |

[16] | Neural Galerkin Schemes for High-Dimensional Evolution Equations. In Computational and Applied Mathematics Colloquium, University of Chicago, Chicago, IL, 2022. |

[17] | Neural Galerkin Schemes for High-Dimensional Evolution Equations. In Applied Mathematics and Computation Seminar, University of Massachusetts Amherst, online, 2022. |

[18] | Neural Galerkin and Active Learning for High-Dimensional Evolution Equations. In SIAM Conference on Mathematics of Data Science, San Diego, CA, 2022. |

[19] | Active learning for solving high-dimensional evolution equations. In 30th Birthday of Acta Numerica, Banach Centre, Poland, 2022. |

[20] | Multifidelity uncertainty quantification. In AIAA Workshop on Multifidelity modeling in support of design and uncertainty quantification, AIAA Aviation, Chicago, IL, 2022. |

[21] | Multilevel Stein variational gradient descent with applications to Bayesian inverse problems. In Erwin Schrödinger International Institute for Mathematics and Physics: Computational Uncertainty Quantification: Mathematical Foundations, Methodology & Data, Vienna, Austria, 2022. |

[22] | Neural Galerkin schemes with active learning for high-dimensional evolution equations. In Data-driven Physical Simulations (DDPS) seminar, online, 2022. |

[23] | Neural Galerkin for Solving Partial Differential Equations with Local Transport-Dominated Dynamics. In SIAM Conference on Uncertainty Quantification, Atlanta, GA, 2022. |

[24] | Active learning for solving high-dimensional evolution equations. In Atmosphere Ocean Science Colloquium, New York University, New York, NY, 2022. |

[25] | Neural Galerkin for Solving Partial Differential Equations with Local Transport-Dominated Dynamics. In SIAM Conference on Analysis of Partial Differential Equations, online, 2022. |

[26] | Establishing trust in decisions made from data: Physics-informed machine-learning models with computable generalization bounds. In Mathematics of Soft Matter, Institute for Mathematical and Statistical Innovation, online, 2022. |

[27] | Scientific machine learning for solving high-dimensional evolution equations. In Graduate Student and PostDoc Seminar, Courant Institute of Mathematical Sciences, New York University, New York, NY, 2022. |

[28] | Scientific machine learning for high-dimensional evolution equations. In Computational and Applied Mathematics Colloquium, Pennsylvania State University, online, 2022. |

[29] | Nonlinear model reduction for chemically reacting flows. In Panel on Data-Driven Methods for Chemically Reacting Flows, AIAA SciTech Forum, online, 2022. |

[30] | Nonlinear model reduction for transport-dominated problems. In RAMSES: Reduced order models; Approximation theory; Machine learning; Surrogates, Emulators and Simulators, online, 2021. |

[31] | Physics-informed machine learning for quickly simulating transport-dominated physical phenomena. In Data-Driven Methods for Science and Engineering Seminar, University of Washington, online, 2021. |

[32] | Scientific machine learning with operator inference and re-projection. In Mathematics Colloquium, Swiss Distance University, online, 2021. |

[33] | Establishing trust in decisions made from data: Certifying physics-informed models learned from data with computable generalization bounds. In IACM Conference on Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology, online, 2021. |

[34] | Context-aware model reduction for uncertainty quantification. In SIAM Annual Meeting 2021, online, 2021. |

[35] | Scientific machine learning with operator inference and re-projection. In ISC High Performance 2021, online, 2021. |

[36] | Scientific machine learning with operator inference and re-projection. In Next Generation Simulation seminar series, Siemens AG, online, 2021. |

[37] | Modeling nonlinear low-dimensional dynamics with deep networks. In Mathematical Modeling and Simulation Seminar, Courant Institute, online, 2021. |

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

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

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

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

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

[43] | 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. |

[44] | 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. |

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

[46] | 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. |

[47] | 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. |

[48] | 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. |

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

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

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

[52] | 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. |

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

[54] | 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. |

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

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

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

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

[59] | 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. |

[60] | 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. |

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

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

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

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

[65] | 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. |

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

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

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

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

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

[71] | 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. |

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

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

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

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

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

[77] | 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. |

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

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

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

[81] | Multifidelity Methods for Uncertainty Quantification. In SIAM Uncertainty Quantification 2016, Lausanne, Switzerland, 2016. |

[82] | Multifidelity Monte Carlo estimation with multiple surrogate models. In Copper Mountain conference on iterative methods, Copper Mountain, USA, 2016. |

[83] | Multifidelity methods for uncertainty quantification. In Third International Workshop on Model Reduction for Parametrized Systems (MoRePaS III) SISSA, Trieste, Italy, 2015. |

[84] | Online adaptive model reduction with dynamic models and sparse sampling. In European Numerical Mathematics and Advanced Applications (ENUMATH) Middle East Technical University, Ankara, Turkey, 2015. |

[85] | Multifidelity Monte Carlo. In 6th Workshop on High-Dimensional Approximation University of Bonn, Bonn, Germany, 2015. |

[86] | Detecting and Adapting to Parameter Changes for Reduced Models of Dynamic Data-driven Application Systems. In International Conference on Computational Science Reykjavík University, Reykjavík, Iceland, 2015. |

[87] | Online Adaptive Model Reduction. In SIAM Conference on Computational Science and Engineering 2015 SIAM, Salt Lake City, USA, 2015. |

[88] | Nonlinear model reduction through online adaptivity and dynamic models. In Scientific Computing Colloquium TUM, Munich, Germany, 2014. |

[89] | Online Adaptive Model Reduction for Nonlinear Systems. In SIAM MIT Chapter 2014 MIT, Boston, USA, 2014. |

[90] | Sparse grid density estimation with data independent quantities. In Sparse Grids and Applications 2014 SimTech, Stuttgart, Germany, 2014. |

[91] | Density Estimation with Adaptive Sparse Grids for Large Datasets. In SIAM Data Mining 2014 SIAM, Philadelphia, USA, 2014. |

[92] | Density Estimation with Adaptive Sparse Grids. In SIAM Uncertainty Quantification 2014 SIAM, Savannah, USA, 2014. |

[93] | Localized model order reduction with machine learning methods. In SIAM and MIT CCE series Center for Computational Engineering, MIT, MIT, Boston, USA, 2014. |

[94] | Localized Discrete Empirical Interpolation Method. In ACDL Seminars Department of Aeronautics and Astronautics, MIT, Department of Aeronautics and Astronautics, MIT, Boston, USA, 2014. |

[95] | Localized DEIM based on feature extraction. In Model Reduction and Approximation for Complex Systems 2013 Institut für Informatik, Technische Universität München, Centre International de Rencontres Mathematiques, Marseille, France, 2013. |

[96] | Tackling higher dimensionalities with sparse grids. In ACM/FEF 2013, San Diego, USA, 2013. |

[97] | Density Estimation for Large Datasets with Sparse Grids. In SIAM Conference on Computational Science and Engineering Institut für Informatik, Technische Universität München, Boston, USA, 2013. |

[98] | Dünne Gitter: Konstruktion und Anwendung optimaler Diskretisierungen. In NUMET 2013 Lehrstuhl für Strömungsmechanik (LSTM)Institut für Informatik, Technische Universität München, Lehrstuhl für Strömungsmechanik (LSTM), Universität Erlangen-Nürnberg, Germany, 2013. |

[99] | Reduced Order Models with LDEIM for Parametrized PDEs with Nonlinear Terms. In Angewandte Analysis und Numerische Simulation Institut für Informatik, Technische Universität München, Universität Stuttgart, Germany, 2013. |

[100] | Localized Discrete Empirical Interpolation Method. In Second International Workshop on Model Reduction for Parametrized Systems (MoRePaS II) Institut für Informatik, Technische Universität München, Schloss Reisensburg, Günzburg, Germany, 2012. |

[101] | Clustering Based on Density Estimation with Sparse Grids. In KI 2012: Advances in Artificial Intelligence Institut für Informatik, Technische Universität München, Saarbrücken, Germany, 2012. |

[102] | A Sparse-Grid-Based Out-of-Sample Extension for Dimensionality Reduction and Clustering with Laplacian Eigenmaps. In SGA 2012 Institut für Informatik, Technische Universität München, Munich, Germany, 2012. |

[103] | A multigrid method for PDEs on spatially adaptive sparse grids. In 28th GAMM-Seminar on Analysis and Numerical Methods in Higher Dimensions Institut für Informatik, Technische Universität München, Leipzig, Germany, 2012. |

[104] | Clustering of Truck-Data with Sparse Grids. In Project Meeting BMBF SIMDATA-NL Institut für Informatik, Technische Universität München, Fraunhofer SCAI, Bonn, Germany, 2011. |

[105] | A multigrid method for PDEs on spatially adaptive sparse grids. In 4th Workshop on High-Dimensional Approximation Fakultät für Informatik, Technische Universität München, Bonn, Germany, 2011. |

[106] | Reduced Basis Methods and Sparse Grids. In HIM - Workshop on Sparse Grids and Applications Fakultät für Informatik, Technische Universität München, Bonn, Germany, 2011. |

[107] | Hierarchical Transformation Multigrid. In Ferienakademie Fakultät für Informatik, Technische Universität München, Durnholz, Italy, 2010. |

[108] | Introduction to Reduced Basis Methods. In Ferienakademie Fakultät für Informatik, Technische Universität München, Durnholz, Italy, 2010. |

[1] | CaCI: Context-aware Controller Inference for Stabilizing Dynamical Systems. In Mathematical and Scientific Machine Learning, Providence, RI, 2023. (poster). |

[2] | Context-aware learning for stabilizing dynamical systems from scarce data. In Workshop and Conference on Nonlinear Model Reduction for Control, Virginia Tech, Blacksburg, VA, 2023. |

[3] | Context-Aware Learning of Stabilizing Controllers in the Scarce Data Regime. In SIAM Conference on Computational Science and Engineering (CSE23), Amsterdam, The Netherlands, 2023. |

[4] | Learning mechanical systems using structured barycentric forms. In Joint Mathematics Meetings (JMM 2023), Boston, MA, 2023. |

[5] | Stabilizing Dynamical Systems in the Scarce Data Regime. In SIAM Conference on Mathematics of Data Science (MDS22), San Diego, SA, 2022. |

[6] | Context-aware learning of low-dimensional stabilizing controllers in the scarce data regime. In Model Reduction and Surrogate Modeling (MORe), Berlin, Germany, 2022. |

[7] | Structured Vector Fitting Framework for Mechanical Systems. In 10th Vienna International Conference on Mathematical Modelling (MATHMOD), Vienna, Austria, 2022. |

[8] | Stabilizing Dynamical Systems in the Scarce Data Regime. In Workshop on New Trends in Computational Science in Engineering and Industrial Mathematics, Magdeburg, Germany, 2022. |

[9] | Stabilizing Dynamical Systems in the Scarce Data Regime. In ICERM Spring 2020 Reunion Event, Institute for Computational and Experimental Research in Mathematics, Providence, RI, 2022. |

[10] | A New Tangential Interpolation Framework for Model Reduction of Bilinear Systems. In 3rd Workshop on Optimal Control of Dynamical Systems and Applications, Osijek, Croatia, 2022. |

[11] | Stabilizing Dynamical Systems in the Scarce Data Regime. In Numerical Analysis and Scientific Computing seminar, Courant Institute, New York University, New York, NY, 2022. |

[12] | Structure-Preserving Learning of Mechanical Systems. In SIAM Conference on Mathematics of Data Science (MDS22), San Diego, SA, 2022. (poster). |

[13] | Balancing-related model reduction of large-scale sparse systems in MATLAB and Octave with the MORLAB toolbox. In Model Reduction and Surrogate Modeling (MORe), Berlin, Germany, 2022. (poster). |

[14] | Context-Aware Learning of Stabilizing Controllers from Data. In SIAM Conference on Uncertainty Quantification, online, 2022. |

[15] | Multilevel Stein Variational Gradient Descent with Applications to Bayesian Inverse Problems. In SIAM Conference on Uncertainty Quantification, online, 2022. |

[16] | Learning Low-Dimensional Models from Noisy State Trajectories with Operator Inference and Re-Projection. In SIAM Conference on Uncertainty Quantification, online, 2022. |

[17] | Trading-off deterministic preconditioning and sampling in Bayesian inference. In The Mathematics of Soft Matter, Institute for Mathematical and Statistical Innovation, online, 2022. (poster). |

[18] | Establishing trust in models learned from data via non-intrusive model reduction with operator inference and re-projection. In RAMSES: Reduced order models; Approximation theory; Machine learning; Surrogates, Emulators and Simulators, online, 2021. (poster). |

[19] | Multifidelity Monte Carlo estimation of energetic particle confinement in stellarators. In Sherwood Fusion Theory Conference, online, 2021. |

[20] | Active operator inference for learning low-dimensional dynamical-system models from noisy data. In IACM Conference on Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology, online, 2021. |

[21] | Multilevel Stein variational gradient descent with applications to Bayesian inverse problems. In Mathematical and Scientific Machine Learning (MSML21), online, 2021. |

[22] | Operator Inference for Learning Reduced Models with Non-Markovian Terms from Partially Observed State Trajectories. In SIAM Annual Meeting 2021, online, 2021. |

[23] | Context-Aware Learning of Models for Data-Driven Robust Control. In SIAM Conference on Control and Its Applications 2021, online, 2021. |

[24] | 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. |

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

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

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

[28] | 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. |

[29] | 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). |

[30] | 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. |

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

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

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

[34] | 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. |

[35] | 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. |

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

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

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

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