2022, April 1, Babuška Forum, The University of Texas at Austin, Austin, US.
Hosted by Anna Yesypenko
Title: Fast and scalable computational methods for learning and optimization under uncertainty.
2022, March 22, UQ hybrid seminar, RWTH Aachen University, online, US.
Hosted by Prof. Raúl Tempone
Title: Fast and scalable computational methods for learning and optimization under uncertainty.
2021, December 13, Numerical Analysis Seminar, East China Normal University, online, US.
Hosted by Prof. Shengfeng Zhu
Title: Fast and scalable computational methods for learning and optimization under uncertainty.
2021, November 13, Seminar for Applied Mathematics/Science Slam, ETH Zurich, online, US.
Hosted by Prof. Ralf Hiptmair
Title: Dimension reduction for Bayesian inference.
2021, May 5, Mathematics Seminar, Xi'an Jiaotong University, online, US.
Hosted by Prof. Junxiong Jia
Title: Fast and scalable computational methods for learning and optimization under uncertainty.
2021, April 12, SCAN Seminar, Cornell University, online, US.
Hosted by Prof. Alex Townsend
Title: Projected Variational Methods for High-dimensional Bayesian Inference.
2021, February 10, CliMA Seminar, California Institute of Technology, online, US.
Hosted by Prof. Andrew Stuart
Title: Derivative-informed projected neural networks for high-dimensional parametric maps governed by PDEs.
2020, September 16, Optimal Transport and Mean Field Game Seminar, University of California, Los Angeles, online, US.
Hosted by Prof. Wuchen Li
Title: Break the curse of dimensionality of Bayesian inference by projected variational transport methods, with application in COVID-19.
2019, April 26, Applied and Computational Mathematics Seminar, University of South Carolina, Columbia, US.
Hosted by Prof. Wolfgang Dahmen
Title: Hessian in action for model reduction, stochastic optimization, and Bayesian inversion.
2019, March 28, Computational Mathematics Seminar, Peking University, Beijing, China.
Hosted by Prof. Jun Hu
Title: Towards Breaking the Curse of Dimensionality for PDE-constrained Optimization under High-dimensional Uncertainty
2017, December 28, Numerical Analysis Seminar, Southern University of Science and Technology, Shenzhen, China.
Hosted by Prof. Jingzhi Li
Title: Towards breaking the curse of dimensionality: Sparse polynomial and reduced basis approximations.
2017, December 26, Numerical Analysis Seminar, East China Normal University, Shanghai, China.
Hosted by Prof. Shengfeng Zhu
Title: Towards breaking the curse of dimensionality: Sparse polynomial and reduced basis approximations.
2017, December 25, Numerical Analysis Seminar, Shanghai Jiao Tong University, Shanghai, China.
Hosted by Prof. Jinglai Li
Title: Taylor approximation and variance reduction for PDE-constrained optimal control under uncertainty.
2016, April 4, Numerical Analysis Seminar, The University of Maryland, College Park, US.
Hosted by Prof. Howard Elman
Title: Computational reduction for PDE-constrained optimal control under uncertainty.
2016, December 5, Applied Math & Analysis Seminar, Duke University, Raleigh, US.
Hosted by Prof. Tingran Gao
Title: Sparse quadrature for high-dimensional integration with Gaussian measure--breaking the curse of dimensionality.
2016, September 8, Numerical Analysis Seminar, North Carolina State University, Raleigh, US.
Hosted by Prof. Alen Alexanderian
Title: Adaptive sparse quadrature for high-dimensional integration with Gaussian distribution--application to Bayesian inverse problems.
2015, May 3, ICES Seminar, The University of Texas at Austin, Austin, US.
Hosted by Prof. Omar Ghattas
Title: Adaptive sparse grid, reduced basis for Bayesian inverse problems.
2015, March 20, Farhat Research Group, Stanford University, Stanford, US.
Hosted by Prof. Charbel Farhat
Title: Adaptive sparse grid, reduced basis approximation for Bayesian inverse problems: on convergence.
2015, March 11, Computing + Mathematical Science, California Institute of Technology, Pasadena, US.
Hosted by Prof. Thomas Yizhao Hou
Title: Adaptive sparse grid, reduced basis approximation for Bayesian inverse problems.
2014, November 17, Mathematical Interdisciplinary Research at Warwick, University of Warwick, Conventry, UK.
Hosted by Prof. Claudia Schillings
Title: Sparse grid, reduced basis approximation for Bayesian inverse problems.
2014, March 10, Seminar for Applied Mathematics, ETH Zurich, Zurich, Switzerland.
Hosted by Prof. Christoph Schwab
Title: Reduced basis methods for uncertainty quantification problems.
2013, May 6, ICMSEC, Chinese Academy of Science, Beijing, China.
Hosted by Prof. Tao Zhou
Title: Reduced basis methods and several extensions for uncertainty quantification problems.
2021, March 1-5, SIAM Conference on Computational Science & Engineering, Virtually.
Title: Taylor Approximation for Chance Constrained Optimization.
2020, May 4-June 30, SIAM conference on Mathematics of Data Science, Virtually
Title: Projected Stein variational methods for high-dimensional Bayesian inverse.
2019, July 15-19, International Congress on Industrial and Applied Mathematics, Valencia, Spain.
Title: Data-drive reduced basis method for variational Bayesian inference.
2019, July 8-12, Applied Inverse Problems, Grenoble, France.
Title: A Stein variational newton method for optimal experimental design.
2019, February 25-March 1, SIAM Conference on Computational Science and Engineering (CSE19), Spokane, US.
Title: Breaking the curse of dimensionality for PDE-constrained optimization under uncertainty.
2018, October 7-10, The 4th Annual meeting of SIAM Central States Section, Oklahoma, US.
Title: Sparse Quadrature for High-dimensional Bayesian Inverse Problems.
2018, April 16-19, SIAM Conference on Uncertainty Quantification, Garden Grove, US.
Title: Scalable Approximation of PDE-Constrained Optimization Under Uncertainty: Application to Turbulent Jet Flow.
2018, March 19-23, GAMM Annual meeting, Munich, Germany.
Title: Taylor Approximation for PDE-Constrained Optimization Under Uncertainty: Application to a Turbulence Model.
2017, February 27-March 3, SIAM Conference Conference on Computational Science & Engineering (CSE17), Atlanta, US.
Title: Taylor Approximation for PDE-Constrained Optimal Control Problems Under High-Dimensional Uncertainty: Application to a Turbulence Model.
2016, April 5-8, SIAM Conference on Uncertainty Quantification, Lausanne, Switzerland.
Title: Sparse Grid, Reduced Basis Bayesian Inversion.
2015, March 14-18, SIAM Conference on Computational Science and Engineering, Utah, US.
Title: Sparse grid, reduced basis method in Bayesian inverse problems.
Title: Reduced basis methods for uncertainty quantification problems.
2014, March 31-April 3, SIAM Conference on Uncertainty Quantification, Savannah, US.
Title: Reduced Basis Method and Several Extensions for Uncertainty Quantification Problems.
Title: Multilevel and weighted reduced basis method for optimal control problems constrained by stochastic PDEs.
Title: Reduced order methods for modelling and computational reduction in UQ problems.
2013, September 1-6, Domain Decomposition Methods for Optimization with PDE Constraints, Monte Verita, Ascona, Switzerland.
Title: Weighted reduced basis method for stochastic optimal control problems with PDE constraints.
2013, August 26-30, European Conference on Numerical Mathematics and Advanced Applications, EPFL, Lausanne, Switzerland.
Title: A weighted reduced basis method for elliptic partial differential equations with random input data.
2013, June 11-14, International Symposium on Modelling of Physiological Flows, Chia Laguna, Italy.
Title: Uncertainty quantification of human arterial system.
Workshops
2021, February 8-12, Optimization under Uncertainty: Learning and Decision Making, Banff, Canada.
Title: Taylor approximation for PDE and chance constrained optimization under uncertainty.
2020, April 20-24, High Dimensional Hamilton-Jacobi PDEs
Workshop II: PDE and Inverse Problem Methods in Machine Learning, Los Angeles, US.
Title: Projected Stein variational methods for high-dimensional Bayesian inversion constrained by large-scale PDEs.
2019, November 11-15, Optimization and Inversion under Uncertainty, Linz, Austria.
Title: Scalable Approximation of PDE-Constrained Optimization Under Uncertainty.
2017, December 18-20, BICMR Young Researchers Workshop, Beijing, China.
Title: Scalable Approximation of PDE-Constrained Optimization Under Uncertainty: Application to Turbulent Jet Flow.
2017, May 2-5, Congress in honor of Yvon Maday for his 60th birthday, Station biologique de Roscoff, Friance.
Title: Hessian-based model reduction.
2017, July 18-21, Quantification of Uncertainty: Improving Efficiency and Technology, Trieste, Italy.
Title: Hessian-based model reduction.
2016, October 4-7, Miami, 4th Workshop on Sparse Grid and Applications, Miami, US.
Title: Adaptive sparse quadrature for high-dimensional integration with Gaussian distribution- application to Bayesian inverse problems.
2016, July 18-22, Stochastic numerical algorithms, multiscale modeling and high-dimensional data analytics, Providence, US.
Title: Adaptive sparse quadrature for high-dimensional integration with Gaussian distribution- application to Bayesian inverse problems.
2014, September 1-4, 3rd Workshop on Sparse Grids and Applications, Stuttgart, Germany.
Title: Model order reduction in uncertainty quantification.
2013, May 13-17, Numerical Methods for Uncertainty Quantification, Bonn University, Bonn, Germany.
Title: Reduced basis methods for reliability analysis.
2013, April 5, Swiss Numerics Colloquium, EPFL, Lausanne, Switzerland.
Title: Accurate and efficient evaluation of failure probability for partial differential equations with random inputs.
2012, October 9-13, Workshop on Uncertainty Quantification, ICERM, Brown University, Providence, US.
Title: Uncertainty quantification of human arterial network.
2012, May 14-16, Workshop on Reduced Basis, POD and Reduced Order Methods for model and computational reduction: towards real-time computing and visualization? EPFL, Lausanne, Switzerland.
Title: Comparison of reduced basis method and stochastic collocation method for stochastic elliptic problems.
2012, April 13, Swiss Numerics Colloquium, University of Bern, Bern, Switzerland.
Title: Stochastic optimal Robin boundary control problems constrained by an advection-dominated elliptic equation.
Co-organized minisymposiums
2022, July 31-August 5, WCCM-APCOM, 15th World Congress on Computational Mechanics & 8th Asian Pacific Congress on Computational Mechanics, Yokohama, Japan.
Co-organizers: Thomas O'Leary-Roseberry and Omar Ghattas
Title: Advances in scientific machine learning for high dimensional many-query problems
2022, April 12-15, SIAM Conference on Uncertainty Quantification (UQ22), Atlanta, US.
Co-organizers: Matthias Chung, Xun Huan, Omar Ghattas, and Youssef Marzouk
Title: Model-Based Optimal Experimental Design.
2022, April 12-15, SIAM Conference on Uncertainty Quantification (UQ22), Atlanta, US.
Co-organizers: Thomas O'Leary-Roseberry and Omar Ghattas
Title: Deep Learning for High-Dimensional Parametric PDEs.
2022, April 12-15, SIAM Conference on Uncertainty Quantification (UQ22), Atlanta, US.
Co-organizers: Omar Ghattas and Johannes Royset
Title: PDE-constrained optimization under uncertainty.
2021, July 25-29, 16th U.S. National Congress on Computational Mechanics, virtually.
Co-organizers: Xun Huan, Omar Ghattas, and Youssef Marzouk
Title: Optimal experimental design in computational science and engineering.
2021, July 20-23, SIAM Conference on Optimization, virtually.
Co-organizers: Thomas O'Leary-Roseberry and Omar Ghattas
Title: Beyond First Order Methods in Large-Scale Stochastic Optimization.
2021, July 20-23, SIAM Annual Meeting, virtually.
Co-organizers: Thomas O'Leary-Roseberry, Omar Ghattas
Title: Deep Learning for High-Dimensional Parametric PDEs.
2021, March 1-5, SIAM Conference on Computational Science and Engineering, virtually.
Co-organizers: Xun Huan, Omar Ghattas, and Youssef Marzouk
Title: Optimal experimental design in computational science and engineering.
2021, January 11-15, 14th World Congress on Computational Mechanics and 8th European Congress on Computational Methods in Applied Sciences and Engineering, virtually.
Co-organizers: Xun Huan, Omar Ghattas, and Youssef Marzouk
Title: Optimal experimental design in computational science and engineering.
2019, July 15-19, The International Congress on Industrial and Applied Mathematics (ICIAM), Valencia, Spain.
Co-organizers: Omar Ghattas
Title: PDE-constrained optimization under uncertainty.
2019, July 8-12, Applied Inverse Problems Conference, Grenoble, France.
Co-organizers: Omar Ghattas and Youssef Marzouk
Title: Recent advances on large-scale Bayesian optimal experimental design.
2019, February 25-March 1, SIAM Conference on Computational Science & Engineering (CSE19), Spokane, US.
Co-organizers: Omar Ghattas
Title: Advances in Reduced Order Modeling for Uncertainty Quantification.
2018, April 16-19, SIAM Conference on Uncertainty Quantification (UQ18), Garden Grove, US.
Co-organizers: Gianluigi Rozza and Omar Ghattas
Title: Advances in Reduced Order Modeling for Uncertainty Quantification.
2017, February 27-March 8, SIAM Conference on Computational Science & Engineering (CSE17), Atlanta, US.
Co-organizers: Georg Stadler and Omar Ghattas
Title: PDE-Constrained Optimal Control Under Uncertainty.
2016, April 5-8, SIAM Conference on Uncertainty Quantification (UQ16), Lausanne, Switzerland.
Co-organizers: Irina K. Tezaur and Gianluigi Rozza
Title: Reduced Order Modelling for UQ PDEs Problems: Optimization, Control, Data Assimilation.
2015, August 10-14, The International Congress on Industrial and Applied Mathematics (ICIAM), Beijing, China.
Co-organizers: Alfio Quarteroni and Gianluigi Rozza
Title: Reduced order modeling in UQ and CFD.
