News

2023

• (2023/05) Jiankan Liao gave a presentation “A Bayesian Approach to Modeling Unit Manufacturing Process Environmental Impacts using Limited Data with Case Studies on Laser Power Bed Fusion Cumulative Energy Demand” at CIRP. [paper]
• (2023/05) ICODOE
  • Thomas Coons gave a presentation “A Multi-Fidelity Approach to Bayesian Optimal Experimental Design”.
  • Jiayuan Dong gave a presentation “Variational Bayesian Optimal Experimental Design with Normalizing Flows”.
  • Xun Huan gave a presentation “Variational Bayesian Sequential Optimal Experimental Design via Policy Gradient”.
  • Xun Huan organized a session “Variational Methods for Optimal Experimental Design”.
• (2023/04) Tahera Hossain and Wanggang Shen gave a presentation “A Bayesian Approach for Quantifying Data Scarcity when Modeling Human Behavior via Inverse Reinforcement Learning” at CHI 2023.
• (2023/03) Xun Huan gave a presentation “A Reinforcement Learning Framework for Bayesian Sequential Experimental Design” at the National University of Singapore Centre for Data Science and Machine Learning (CDSML) Seminar.
• (2023/03) Snehal Prabhudesai gave a presentation “Understanding Uncertainty: How Lay Decision-makers Perceive and Interpret Uncertainty in Human-AI Decision Making” at IUI 2023.
• (2023/03) Sanat gave a presentation “Understanding capacity fade in organic redox-flow batteries by combining spectroscopy with statistical inference techniques” at ACS Spring 2023.
• (2023/03) SWQU Spring Meeting
  • Hongfan Chen presented a poster “Connecting White Light Images with In-situ Observations of Solar Wind Quantities for CME Simulations in the SWMF”.
  • Aniket Jivani presented a poster “Uncertainty Quantification & Global Sensitivity Analysis for CME Simulations in the SWMF”.
• (2023/02) SIAM CSE
  • Atlanta Chakraborty gave a presentation “An Approximate Bayesian Computation Approach to Goal-Oriented Bayesian Experimental Design”.
  • Thomas Coons gave a presentation “A Multifidelity Approach to Model-Based Bayesian Optimal Experimental Design”.
  • Jiayuan Dong gave a presentation “Variational Bayesian Optimal Experimental Design with Normalizing Flows”.
  • Jeremiah Hauth gave a presentation “Stein VI for Bayesian Neural Network Prediction of Helicopter Rotor Performance”.
  • Xun Huan gave a presentation “Reinforcement Learning Framework for Bayesian Sequential Optimal Experimental Design”.
  • Chengyang Huang gave a presentation “Inverse Reinforcement Learning Via Variantioanl System Identification of Fokker-Planck Equation”.
  • Aniket Jivani gave a presentation “Uncertainty Quantification for Random Field Quantities Using Multi-Fidelity Karhunen-Loève Expansions with Active Learning”.
  • Codie Kawaguchi gave a presentation “Quantifying and Reducing Uncertainty in Inertial Confinement Fusion Experiments Using Optimal Experimental Design”.
  • Snehal Prabhudesai gave a presentation “Lowering The Computational Barrier : Selective Bayesian Uncertainty Quantification for Transparency in Medical Imaging AI”.
  • Xun Huan co-organized a two-part minisymposium “Model-Based Optimal Experimental Design” (parts I, II).
• (2023/01) Chengyang Huang presented a poster “Leveraging Artificial Intelligence to Understand Single Cell State Transitions and Predict Cell State” at the BMES CMBE Conference.

2022

• (2022/12) Yummy food at Namaste Flavours!
• (2022/12) AGU Fall Meeting
  • Hongfan Chen gave a presentation “Connecting White Light Images with In-situ Observations of Solar Wind Quantities for CME Simulations in the SWMF”.
  • Aniket Jivani presented a poster “Uncertainty Quantification and Global Sensitivity Analysis for CME simulations in the SWMF”.
  • Gabor Toth presented a poster “Developing the Next Generation Space Weather Modeling Framework: Year 2”.
• (2022/11) Congratulations to Codie Kawaguchi, Shuyu Long, and Thomas Coons for successfully passing the Ph.D. Qualifying Exam!
• (2022/10) Xun Huan gave a presentation “Goal-Oriented Optimal Experimental Design for Nonlinear Physical Systems” at SES Annual Technical Meeting.
• (2022/09) Xun Huan gave a presentation “Bayesian Reinforcement Learning for Optimal Sensor Relocation in Convection-Diffusion Fields” at SIAM MDS.
• (2022/08) Xun Huan gave a presentation “Multifidelity Karhunen-Loeve Expansion Surrogate Models for Uncertainty Propagation” at the Joint Statistical Meetings.
• (2022/08) WCCM-APCOM Congress
  • Siddhartha Srivastava gave a presetation “Discovery of Cell Migration Models by Data Driven Variational System Identification and Inverse Reinforcement Learning”.
  • Christian Jacobsen gave a presetation “Variational Bayesian optimal experimental design for the discovery of electro-deposition process models”.
• (2022/07) Our paper “Multi-fidelity uncertainty quantification of particle deposition in turbulent pipe flow” is published in Journal of Aerosol Science.
• (2022/07) Our paper “A Bayesian Approach for Quantifying Data Scarcity when Modeling Human Behavior via Inverse Reinforcement Learning” is published in ACM Transactions on Computer-Human Interaction.
• (2022/06) Daniel Cooper gave a presentation “Expert Elicitation and Data Noise Learning for Material Flow Analysis using Bayesian Inference” at ISSST 2022.
• (2022/06) Beckett Zhou gave a presentation “A Data-Driven Approach for Enhancement of Propeller Performance Prediction” at the AIAA/CEAS Aeroacoustics Conference. [paper]
• (2022/06) Xun Huan presented a poster “Goal-Oriented Optimal Experimental Design for Nonlinear Systems” at the ISBA World Meeting.
• (2022/06) Aniket Jivani presented a poster “Global Sensitivity Analysis for Solar Wind Simulations in the Space Weather Modeling Framework” at the SHINE 2022 Workshop.
• (2022/05) End-of-semester celebration at the Grove!
• (2022/04) SIAM UQ
  • Andrew Davis gave a presentation “Local Approximation of Expected Utility Surface for Nonlinear Bayesian Optimal Experimental Design”.
  • Jiayuan Dong gave a presentation “Expert Elicitation and Opinion Aggregation for Bayesian Prior Construction with Application to the U.S. Steel Flow Analysis”.
  • Jeremiah Hauth gave a presentation “Efficient Large-Scale Bayesian Inference for Predictive Modeling in Precision Health Balance Training”.
  • Xun Huan gave a presentation “Goal-Oriented Optimal Experimental Design for Nonlinear Models using MCMC”.
  • Chengyang Huang gave a presentation “Fast Approximate Bayesian Uncertainty Quantification Using Conditional Generative Adversarial Networks”.
  • Aniket Jivani gave a presentation “Sobol’ Sensitivity for Uncertain Model Parameters in Simulations of Background Solar Wind”.
  • Snehal Prabhudesai gave a presentation “Partially Bayesian Neural Networks: Low-Cost Bayesian Uncertainty Quantification for Deep Learning in Medical Image Segmentation”.
  • Wanggang Shen gave a presentation “Optimal Bayesian Design of Sequential Experiments Using Deep Deterministic Policy Gradient”.
  • Maria Han Veiga gave a presentation “Optimal Experimental Design Using Variational Inference Approximations”.
  • Xun Huan co-organized a three-part minisymposium “Model-Based Optimal Experimental Design” (parts I, II, III).
• (2022/03) Patrick Kinnunen and Lucy Spicher are presentating at the MIDAS PODS Showcase.
• (2022/02) A fun group dinner at Cafe Zola!
• (2022/01) Xun Huan gave a presentation “Optimal Bayesian Design of Finitely Sequential Experiments with Deep Deterministic Policy Gradient” at the Royal Statistical Society (RSS) Computational Statistics and Machine Learning session on Automatic Experimentation.
• (2022/01) Our new project has been funded by the W. M. Keck Foundation! (Briefs: 1, 2)
• (2022/01) Xun Huan gave a presentation “Bayesian Recurrent Neural Networks for Monitoring Rotorcraft Icing from Aeroacoustics Time-Series Data” at the AIAA SciTech Forum. [paper]

2021

• (2021/12) Xun Huan gave a presentation “Bayesian Sequential Optimal Experimental Design for Nonlinear Systems via Policy Gradient” at the Applied Reinforcement Learning Seminar.
• (2021/12) AGU Fall Meeting
  • Gabor Toth gave a presentation “Developing the Michigan Sun-to-Earth Model with Data Assimilation and Quantified Uncertainty”.
  • Aniket Jivani presented a poster “Global Sensitivity Analysis for Solar-Wind Simulations in the Space Weather Modelling Framework”.
• (2021/11) Congratulations to Jiayuan Dong for successfully passing the Ph.D. Qualifying Exam!
• (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) Our paper “Stratification by Tumor Grade Groups in a Holistic Evaluation of Machine Learning for Brain Tumor Segmentation” is published in Frontiers in Neuroscience.
• (2021/10) Our preprint “Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning” is available on arXiv.
• (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) Congratulations to Chengyang Huang for successfully passing the Ph.D. Qualifying Exam!
• (2021/08) Congratulations to Saibal De for successfully defending his Ph.D. thesis!
• (2021/08) Our paper “Bayesian Inference of Parameters in Power System Dynamic Model Using Trajectory Sensitivities” is published in IEEE Transactions on Power Systems.
• (2021/08) Xun Huan gave a presentation “Closed-Loop Bayesian Design of Sequential Experiments via Dynamic Programming and Reinforcement Learning” at the IFIP TC7 Conference on System Modelling and Optimization.
• (2021/07) Our preprint “Reconstruction of the Density Power Spectrum from Quasar Spectra using Machine Learning” is available on arXiv.
• (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 Loss-of-Balance Event Identification in Older Adults at Risk of Falls during Real-World 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 Karhunen-Loeve Expansions”.
  • Wanggang Shen gave a presentation “Sequential Optimal Experimental Design Using Reinforcement Learning with Policy Gradient”.
  • Xun Huan co-organized 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 co-organized a two-part minisymposium “Physics-Aware 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 Sea-Level 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 “Model-based 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 Karhunen-Loeve 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 “Tensor-Train Decomposition for Data Compression and Data-Driven 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 co-organized a two-part minisymposium “Model-Based Optimal Experimental Design” (parts I, II).
• (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 Karhunen-Loeve Expansions” at the AIAA SciTech Forum. [paper]
• (2021/01) WCCM-ECCOMAS Congress
  • Wanggang Shen gave a presetation “Sequential Optimal Experimental Design Using Reinforcement Learning with Policy Gradient”.
  • Xun Huan co-organized 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) COVID-19 shutdown.
• (2020/02) Our paper “Uncertainty Propagation Using Polynomial Chaos Expansions for Extreme Sea-level Hazard Assessment: The Case of the Eastern Adriatic Meteotsunamis” is published in Journal of Physical Oceanography.
• (2020/02) Xun Huan gave a presentation “Simulation-based 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 Real-Time 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 “Simulation-based 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 Simulation-based 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 Pattern-forming 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 pattern-formation: 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 Physics-Based 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 Real-Time In-Flight 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 “Entropy-based 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 simulation-based 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 Large-Eddy 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 co-organized a two-part 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 Large-Eddy 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 Model-based 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 Co-Optima 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 Cross-Validation and Stop-Sampling 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 “Simulation-Based 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 “Simulation-based 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 Manchester-Southampton-Glasgow 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 Cross-Validation and Stop-Sampling for Sparse Polynomial Chaos Expansions”.
  • Xun Huan co-organized a three-part minisymposium “Model-Based 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.