2021
 (2021/11) Xun Huan gave a presentation “Reinforcement Learning for Sequential Optimal Experimental Design” at the New England Statistical Society (NESS) NextGen: Data Science Day.
 (2021/10) Xun Huan gave a presentation “Bayesian Optimal Experimental Design for Batch and Sequential Experiments” at the Dartmouth College Applied and Computational Mathematics Seminar.
 (2021/08) Xun Huan gave a presentation “ClosedLoop Bayesian Design of Sequential Experiments via Dynamic Programming and Reinforcement Learning” at the IFIP TC7 Conference on System Modelling and Optimization.
 (2021/07) Our paper “Digital Twin Concepts with Uncertainty for Nuclear Power Applications” is published in Energies (special issue Expanding Nuclear Applications and Technologies for a Clean Energy Future).
 (2021/07) Our paper “Automated LossofBalance Event Identification in Older Adults at Risk of Falls during RealWorld Walking Using Wearable Inertial Measurement Units” is published in Sensors (special issue Wearable Sensors for Gait and Falls Monitoring).
 (2021/07) USNCCM
 Jeremiah Hauth gave a presentation “Variational Bayesian Inference for Convolutional Neural Networks in Precision Health Balance Training”.
 Chengyang Huang gave a presentation “Bayesian Inference via Conditional Generative Adversarial Networks”.
 Aniket Jivani gave a presentation “Uncertainty Quantification for Random Field Quantities Using Multifidelity KarhunenLoeve Expansions”.
 Wanggang Shen gave a presentation “Sequential Optimal Experimental Design Using Reinforcement Learning with Policy Gradient”.
 Xun Huan coorganized a minisymposium “Optimal Experimental Design in Computational Science and Engineering”.
 (2021/07) SIAM Annual Meeting
 Xun Huan gave a presentation “Optimal Experimental Design for Variational System Identification of Material Physics Phenomena”.
 Xun Huan coorganized a twopart minisymposium “PhysicsAware Machine Learning for Solving and Discovering PDEs” (parts I, II).
 (2021/06) ISBA World Meeting

 Xun Huan gave a presentation “Optimal Bayesian Sequential Design Using Reinforcement Learning with Policy Gradient Methods”.
 Chengyang Huang gave a presentation “Fast Approximate Bayesian Inference via Conditional Generative Adversarial Networks”.
 (2021/05) Our paper “Conceptual Design of Extreme SeaLevel Early Warning Systems Based on Uncertainty Quantification and Engineering Optimization Methods” is published in Frontiers in Marine Science.
 (2021/05) EMI/PMR Conference
 Xun Huan gave a presentation “Optimal Sequential Bayesian Design of Experiments Using Reinforcement Learning with Policy Gradient”.
 Wanggang Shen gave a presentation “Optimal Bayesian Experimental Design for Variational System Identification of Materials Physics Phenomena”.
 (2021/04) Xun Huan gave a presentation “Modelbased sequential experimental design” at the USACM TTA Uncertainty Quantification and Probabilistic Modeling Webinar.
 (2021/03) Congratulations to Aniket Jivani for successfully passing the Ph.D. Qualifying Exam!
 (2021/03) Congratulations to Codie Kawaguchi for winning the NSF Graduate Research Fellowships Program (GRFP) award!
 (2021/03) SIAM CSE
 Aniket Jivani gave a presentation “Multifidelity KarhunenLoeve Expansions for Uncertainty Propagation of Random Field Quantities”.
 Maria Veiga gave a presentation “Including Physical Knowledge into Data Driven Models: Applications in Computational Astrophysics”.
 Saibal De presented a poster “TensorTrain Decomposition for Data Compression and DataDriven Reduced Order Modeling”.
 Jeremiah Hauth gave a presentation “Variational Bayesian Inference for Convolutional Neural Networks in Precision Health Balance Training”.
 Xun Huan gave a presentation “Optimal Experimental Design for Variational System Identification of Material Physics Phenomena”.
 Wanggang Shen gave a presentation “Optimal Bayesian Design of Sequential Experiments using Reinforcement Learning with Policy Gradient Methods”.
 Xun Huan coorganized a minisymposium “ModelBased Optimal Experimental Design” (part 1, 2).
 (2021/02) Our paper “Variational system identification of the partial differential equations governing microstructure evolution in materials: Inference over sparse and spatially unrelated data” is published in Computer Methods in Applied Mechanics and Engineering.
 (2021/01) Congratulations to Snehal Prabhudesai for successfully passing the Ph.D. Qualifying Exam!
 (2021/01) Aniket Jivani gave a presentation “Uncertainty Quantification for a Turbulent Round Jet Using Multifidelity KarhunenLoeve Expansions” at the AIAA SciTech Forum [paper].
 (2021/01) WCCMECCOMAS Congress
 Wanggang Shen gave a presetation “Sequential Optimal Experimental Design Using Reinforcement Learning with Policy Gradient”.
 Xun Huan coorganized a minisymposium “Optimal Experimental Design in Computational Science and Engineering”.
2020
 (2020/12) Xun Huan gave a presentation “Optimal Sequential Bayesian Design of Experiments Using Reinforcement Learning with Policy Gradient” at the Machine Learning in Science & Engineering (MLSE) Conference. [video]
 (2020/12) AGU Fall Meeting
 Yulin Pan presented a poster “Optimal Experimental Design of Sensor Locations in EnKF Data Assimilation for Predicting Oceanic Rogue Waves”.
 Gabor Toth gave a presentation “NextGen Space Weather Modeling Framework Using Physics, Data Assimilation, Uncertainty Quantification and GPUs”.
 (2020/03) COVID19 shutdown.
 (2020/02) Our paper “Uncertainty Propagation Using Polynomial Chaos Expansions for Extreme Sealevel Hazard Assessment: The Case of the Eastern Adriatic Meteotsunamis” is published in Journal of Physical Oceanography.
 (2020/02) Xun Huan gave a presentation “Simulationbased Bayesian Sequential Design Using Reinforcement Learning” at the Michigan State University Department of Statistics and Probability Colloquium.
 (2020/01) AIAA SciTech Forum
 Wanggang Shen gave a presentation “Towards Design of Airfoil Pressure Tap Locations for RealTime Predictions Under Uncertainty Using Bayesian Neural Networks” [paper].
 Jeremiah Hauth gave a presentation “Correlation Effects in Bayesian Neural Networks for Computational Aeroacoustics Ice Detection” [paper].
 Beckett Zhou gave a presentation “Correlation Effects in Bayesian Neural Networks for Computational Aeroacoustics Ice Detection” [paper].
2019
 (2019/12) Xun Huan gave a presentation “Simulationbased optimal sequential Bayesian design using policy gradient reinforcement learning” at the International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics).
 (2019/11) Our paper “Stochastic surrogate model for meteotsunami early warning system in the easter Adriatic Sea” is published in Journal of Geophysical Research: Oceans.
 (2019/11) Xun Huan gave a presentation “Finding the Most Useful Data via Simulationbased Bayesian Experimental Design” at the University of Michigan Integrative Systems + Design (ISD) Manufacturing Seminar Series.
 (2019/11) Congratulations to Jeremiah Hauth and Wanggang Shen for successfully passing the Ph.D. Qualifying Exams!
 (2019/09) Our paper “Design optimization of a scramjet under uncertainty using probabilistic learning on manifolds” is published in Journal of Computational Physics.
 (2019/09) Xun Huan gave a presentation “Optimal Bayesian Design of Sequential Experiments Using Policy Gradient Reinforcement Learning” at the University of Notre Dame Center for Informatics and Computational Science (CICS) Seminar.
 (2019/08) Xun Huan gave a presentation “Uncertainty Quantification via Optimal Experimental Design and Bayesian Neural Networks for Aerospace Applications” at the National Institute of Aerospace Computational Fluid Dynamics Seminar.
 (2019/08) Xun Huan gave a tutorial lecture “Introduction to Optimal Experimental Design” at the Uncertainty Quantification Summer School. [slides]
 (2019/07) USNCCM
 Xun Huan gave a presentation “Linear Experimental Design for Optimal Control Variate Based Surrogate Models”.
 Krishna Garikipati gave a presentation “Variational System Identification of the Partial Differential Equations Governing Patternforming Physics: Inference under Varying Fidelity and Noise”.
 Habib Najm gave a presentation “Uncertainty Quantification in Computational Models of Large Scale Physical Systems”.
 (2019/07) Xun Huan gave a presentation “Sequential Optimal Experimental Design via Reinforcement Learning” at the Workshop on Machine Learning and Uncertainty Quantification.
 (2019/07) Our paper “Variational system identification of the partial differential equations governing the physics of patternformation: Inference under varying fidelity and noise” is published in Computer Methods in Applied Mechanics and Engineering.
 (2019/07) Xun Huan gave a presentation “Policy Gradient Acceleration for Sequential Bayesian Experimental Design” at the Applied Inverse Problems Conference.
 (2019/07) Xun Huan gave a presentation “Optimal Experimental Design and Bayesian Neural Networks for PhysicsBased Models” at TU Kaiserslautern Scientific Computing Seminar.
 (2019/06) Our paper “Embedded Model Error Representation for Bayesian Model Calibration” is published in International Journal for Uncertainty Quantification.
 (2019/06) Beckett Zhou gave a presentation “Towards RealTime InFlight Ice Detection Systems via Computational Aeroacoustics and Bayesian Neural Networks” at the AIAA Aviation Forum [paper].
 (2019/05) Wanggang Shen gave a presentation “Optimal Bayesian design of sequential experiments via reinforcement learning” at the International Conference on Design of Experiments (ICODOE).
 (2019/04) Our paper “Entropybased closure for probabilistic learning on manifolds” has been published in Journal of Computational Physics.
 (2019/03) Xun Huan gave a presentation “Finding the most useful data via simulationbased Bayesian experimental design” at the University of Michigan Applied and Interdisciplinary Mathematics (AIM) Seminar.
 (2019/03) SIAM CSE
 Xun Huan gave a presentation “Global Sensitivity Analysis for Random Fields in LargeEddy Simulations of Scramjet Computations”.
 Khachik Sargsyan gave a presentation “Bayesian Inference for Structural Error Quantification”.
 Andrew Davis gave a presentation “Selecting Multiple Borehole Locations for Maximizing Bayesian Information Gain on Groundwater Transmissivity”.
 Xun Huan coorganized a twopart minisymposium “Optimal Experimental Design for Inverse Problems” (parts I, II).
 (2019/01) Our paper “Compressive sensing adaptation for polynomial chaos expansions” has been published in Journal of Computational Physics.
 (2019/01) AIAA SciTech Forum
 Xun Huan gave a presentation “Uncertainty Propagation Using Conditional Random Fields in LargeEddy Simulations of Scramjet Computations” [paper].
 Gianluca Geraci gave a presentation “Progress in Scramjet Design Optimization Under Uncertainty Using Simulations of the HIFiRE Direct Connect Rig” [paper].
2018
 (2018/12) Our paper “Enhancing Model Predictability for a Scramjet Using Probabilistic Learning on Manifolds” has been published in AIAA Journal.
 (2018/12) Andrew Davis gave a presentation “Selecting Multiple Borehole Locations for Maximizing Bayesian Information Gain on Past Ice Sheet Surface Temperatures” at the AGU Fall Meeting.
 (2018/11) Xun Huan gave a presentation “Finding the Most Informative Data Using Modelbased Bayesian Experimental Design” at the Oakland University Department of Physics Colloquium.
 (2018/10) Our paper “Uncertainty Assessment of Octane Index Framework for Stoichiometric Knock Limits of CoOptima Gasoline Fuel Blends” has been published in SAE International Journal of Fuels and Lubricants.
 (2018/08) Jeremiah Hauth and Wanggang Shen joined the group!
 (2018/06) Our paper “Compressive sensing with CrossValidation and StopSampling for Sparse Polynomial Chaos Expansions” has been published in SIAM/ASA Journal on Uncertainty Quantification.
 (2018/03) Our paper “Global Sensitivity Analysis and Estimation of Model Error, Toward Uncertainty Quantification in Scamjet Computations” has been published in AIAA Journal.
 (2018/08) Xun Huan moved to Ann Arbor, MI to start a new position as an Assistant Professor of Mechanical Engineering at the University of Michigan.
 (2018/07) Joint Statistical Meetings
 Xun Huan gave a speed presentation “SimulationBased Bayesian Optimal Design for Ice Sheet Borehole Experiments” (oral and poster parts).
 Khachik Sargsyan gave a presentation “Bayesian Framework for Embedded Model Error Representation and Quantification”.
 (2018/07) SIAM Annual Meeting
 Xun Huan gave a presentation “Optimal Bayesian Experimental Design of Borehole Locations for Inferring Past Ice Sheet Surface Temperature”.
 Cosmin Safta gave a presentation “Adaptive Sparse Quadrature for Multifidelity Scramjet Flow Simulations”.
 Khachik Sargsyan gave a presentation “Bayesian Inference for Model Error Quantification and Propagation with UQTk”.
 (2018/06) Xun Huan presented a poster “Choosing Embedding for Capturing Model Misspecification Using Global Sensitivity Analysis and Bayes Factor Computation” at the ISBA World Meeting.
 (2018/06) Xun Huan gave a presentation “Simulationbased Bayesian Experimental Design for Computationally Intensive Models” at the University of Cambridge Isaac Newton Institute for Mathematical Sciences, Programme on Uncertainty Quantification for Complex Systems: Theory and Methodologies, and as a part of the ManchesterSouthamptonGlasgow Design of Experiments Seminar Series.
 (2018/06) Xun Huan gave a presentation “Value of Feedback and Lookahead in Bayesian Sequential Optimal Experimental Design” at the Joint Research Conference on Statistics in Quality, Industry, and Technology.
 (2018/04) SIAM UQ
 Xun Huan gave a presentation “Compressive Sensing with CrossValidation and StopSampling for Sparse Polynomial Chaos Expansions”.
 Xun Huan coorganized a threepart minisymposium “ModelBased Optimal Experimental Design” (parts I, II, III).
 (2018/02) Xun Huan gave a presentation “Optimal Sequential Bayesian Experimental Design” at the workshop “Foresight for Making Good Future Predictions—Lookahead Optimization in Artificial and Natural Systems” held by the Santa Fe Institute.
 (2018/01) Xun Huan gave a presentation “Multifidelity Statistical Analysis of Large Eddy Simulations in Scramjet Computations” at the AIAA SciTech Forum [paper].
2017
 (2017/12) Andrew Davis gave a presentation “Optimal Experimental Design of Borehole Locations for Bayesian Inference of Past Ice Sheet Surface Temperatures” at the AGU Fall Meeting.