- Worskhop on Optimization under Uncertainty (Virtual), Centre de Recherches Mathematiques (CRM), September 2021.
- “Data-Driven Sample Average Approximation with Covariate Information” [Guzin Bayraksan]
- Plenary: “A Sequential Linearization Approach to Nonlinear Robust Optimization” [Sven Leyffer]

- Institute for Mathematical and Statistical Innovation (IMSI) Workshop on Verification, Validation, and Uncertainty Quantification Across Disciplines, May 2021.
- Organizer: Mihai Anitescu

- SIAM Conference on Computational Science and Engineering CSE21 (Virtual), March
2021.
- “Rare event simulation and control of electricity cascades” [Mihai Anitescu]
- “Using Scoring Rules to Solve Inverse Problems Constrained by Differential Equations with Stochastic Terms” [Emil Constantinescu]
- “Scalable Algorithms for Realtime Optimization of Power Grid Systems using High-Performance Computing” [Cosmin Petra]
- “Machine-learning enhanced paerturbative renormalization” [Panos Stinis]
- “Optimal Experimental Design for Sensor Placement and Acquisition of Highly-Correlated Data” [Ahmed Attia & Emil Constantinescu]
- “Large Deviation Theory for the Analysis of Power Tansmission Systems Subject to Stochastic Forcing” [David A. Barajas-Solano]
- “Gradient-Enhanced Gaussian Process Regression for Scientific Computing” [Xiu Yang]
- Minisymposium organizers: Mihai Anitescu, Emil Constantinescu, Charlotte Haley, Sven Leyffer

- “Rare event simulation of energy cascades using Kinetic Monte Carlo”, IEEE PES societywide seminar, October 2021. [Mihai Anitescu]

- “Exponential Decay of Sensitivity in Dynamic Programming and Computational Consequences”, US Naval Academy Conference in Optimization, June 2021. [Mihai Anitescu]

- “Rare event simulation and control of electricity cascades”, PHILMS virtual seminar, April 2021. [Mihai Anitescu]

- “Residuals-Based Distributionally Robust Optimization with Covariate Information”, The OR Society’s 63rd Annual Conference (OR63) (Virtual), September 2021. [Guzin Bayraksan]

- “Residuals-based sample average approximation with covariate information”, I-Sim Workshop on From Data to Decision-Making: Contending with Uncertainty and Non-Stationarity in Simulation Theory (Virtual), June 2021. [Guzin Bayraksan]

- “Residuals-Based Stochastic Optimization Approaches with Covariate Information”, Department of Industrial Engineering Seminar Series, Clemson University (Virtual), April 2021. [Guzin Bayraksan]

- “Data-Driven Sample Average Approximation with Covariate Information”, Banâ€€ International Research Station (BIRS) Workshop on Optimization under Uncertainty: Learning and Decision Making (Virtual), February 2021. [Guzin Bayraksan]

- Department of Mathematical Sciences seminar, Stevens Institute of Technology (Virtual) March 2021. [Julie Bessac]

- “New Efficient Approaches for Model-Constrained Optimal Design of Experiments”, Argonne National Laboratory, LANS Seminar Series, March 2021. [Ahmed Attia]

- “Optimization in Energy and Environmental Systems”, Weston Roundtable Series, Nelson Institute, October 2021. [Michael Ferris]

- “Computation in Emerging Markets”, University of Southern California, June 2021. [Michael Ferris]

- “Covid allocation review”, Department of Health Services, weekly presentations from December 2020 to April 2021. Also multiple public presentations in March 2021. [Michael Ferris]

- “Overview of Energy Markets”, Wisconsin Public Utilities, April 2021. [Michael Ferris]

- “Dairy Brain | Informing Decisions on Dairy Farms using Data Analytics”, Dairy Cattle Reproduction Council (Virtual), November, 2020, and Cornell University, January 2021. [Michael Ferris]

- “Distributed Optimization for Electric Grid System on HPC”, Workshop on Computational Mission Needs, June 2021. [Kibaek Kim]

- “Distributed Control of Electric Grid System: Challenges, Algorithms, and Computation”, Argonne National Laboratory, June 2021. [Kibaek Kim]

- “On the Tightness and Scalability of the Lagrangian Dual of Structured Nonconvex Optimization”, Arizona State University, March 2021. [Kibaek Kim]

- “Spatial modelling and conditional simulation using R-INLA”, University of Chicago (Virtual seminar), February 2021. [Amanda Lenzi]

- “Improving Bayesian Local Spatial Models in Large Data Sets”, Lawrence Livermore National Laboratory (Virtual seminar), May 2021. [Amanda Lenzi]

- “Orbital Conflict: Cutting Planes for Symmetric Integer Programs”, Virginia Tech University (Virtual), January 2021. [Jeff Linderoth]

- “Perspectives on Integer Programming for Sparse Optimization”, American Family Insurance (Virtual), April 2021. [Jeff Linderoth]

- Plenary: “High-Rank Matrix Completion by Integer Programming”, Modeling and Optimization: Theory and Applications (MOPTA), Bethlehem, PA, August 2021. [Jeff Linderoth]

- “Subspace Clustering with Missing Data via Integer Programming”, Cornell University, October 2021. [Jeff Linderoth]

- “Stochastic integer programming”, Invited lecture, 2021 Grid Science Winter School & Conference, Los Alamos National Laboratory (Virtual), January 2021. [Jim Luedtke]

- “On Solving Large-Scale Nonconvex Stochastic Programming Problems using High-Performance Computing”, SIAM Conference on Optimization OP21, July 2021. [Cosmin Petra]

- “Tackling Infinite-Dimensional Optimization Problems with InfiniteOpt.jl”, TWCCC Semi-Annual Meeting (Virtual), 2021. [Joshua Pulsipher]

- “Modeling and Optimization of Integrated Gas-Electric Networks”, NERC Resources Subcommittee (Virtual), October 2021. [Line Roald]

- “Burning Issues: Modeling Risk of Wildfire Ignitions and Power Outages in the Electric Grid”, Energy Seminar, UC San Diego (Virtual), October 2021. [Line Roald]

- “Stochastic hybrid approximation for Uncertainty Management in Gas-Electric Systems”, 2021 Workshop on Resilient Autonomous Energy Systems, National Renewable Energy Laboratory (Virtual), September 2021. [Line Roald]

- “Parametric Models for Distributions When Extremes Are of Interest”, Institute for Mathematical and Statistical Innovation meeting on Confronting Climate Change (Virtual), March 2021. [Michael Stein]

- “Machine-learning enhanced paerturbative renormalization”, SIAM Conference on Applications of Dynamical Systems DS21 (Virtual), May 2021. [Panos Stinis]

- “Optimal renormalization of multiscale systems”, Dynamics Days Europe (Virtual), August 2021. [Panos Stinis]

- “Physics-Informed Machine Learning Methods for Large-Scale Data Assimilation Problems”, Machine Learning in Solid Earth Geoscience Virtual Lecture Series, October 2021. [Alexandre Tartakovsky]

- “EMS2.0A: Multi-Scale State Estimator”, IEEE PES General Meeting (Virtual), July 2021. [Shaobu Wang]

- ICCV Workshop on Learning for Computational Imaging, 2021. [Rebecca Willett]

- Plenary speaker at StatMathAppli, 2021. [Rebecca Willett]

- Plenary speaker at First International Conference on Statistics and Related Fields, 2021. [Rebecca Willett]

- Workshop on Modern Optimization and Applications, Chinese Academy of Sciences, January 2021. [Stephen Wright]

- OR and Statistics Seminar, US Naval Academy, May 2021. [Stephen Wright]

- Applied Mathematics Colloquium, Caltech, May 2021. [Stephen Wright]

- Plenary speaker at USNA Conference on Optimization and Operations Research, June 2021. [Stephen Wright]

- Online Seminar Series on \Mathematical Foundations of Data Science”, June 2021. [Stephen Wright]

- FMG Data Driven Control Summer School, ETH Zurich, June 2021. [Stephen Wright]

- USC Industrial Engineering Colloquium, September 2021. [Stephen Wright]

- MIT OptML++ Seminar, September 2021. [Stephen Wright]

- “Stochastic Learning Approach for Model-Constrained Optimal Design of Experiment”, SIAM Annual Meeting AN21 (Virtual), July 2021. [Ahmed Attia]

- “An Optimal Experimental Design Framework for Sensor Placement and Acquisition of Highly-Correlated Data”, ECCOMAS Congress 2020 & 14th WCCM Joint Congress (Virtual) January 2021. [Ahmed Attia]

- Joint Statistical Meeting 2021 (Virtual), August 2021. [Julie Bessac]

- “Can neural networks be used for parameter estimation?”, Joint Statistical Meeting, August 2021. [Amanda Lenzi]

- “Short-term forecasting and estimation of power grid states using physics-informed Gaussian process regression”, Physics Informed Machine Learning Workshop (Virtual), February 2021. [Tong Ma]

- “Estimation and Forecasting of Power Grids Using Statistical and Machine Learning Techniques”, Pacific Northwest National Laboratory TechFest 21 (Virtual), July 2021. [Tong Ma]

- “Physics-Informed Gaussian Process Regression for States Estimation and Forecasting in Power Grids”, Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology Conference (Virtual), September 2021. [Tong Ma]

- “InfiniteOpt.jl: A JuMP Extension for Tackling Infinite-Dimensional Optimization Problems”, JuliaCon (Virtual), July 2021. [Joshua Pulsipher]

- “Physics-Informed Machine Learning for High-Dimensional Parameter Estimation Problems”, Mathias Convention on Applied Mathematics, Scientific Computing, Data Science and Artificial Intelligence, Paris, France, October 2021. [Alexandre Tartakovsky]

- “Implementation of State Estimation: A Multi-Scale Framework”, IEEE 2021 International Conference on Smart Grid Synchronized Measurements and Analytics SGSMA, May 2021. [Shaobu Wang]

- “Imposing Physical Constraints Softly on Augmented Gaussian Random Fields”, 16th U.S. National Congress on Computational Mechanics, July 2021. [Xiu Yang]

- Invited tutorial “Stochastic integer programming”, Worskhop on Optimization under Uncertainty (Virtual), Centre de Recherches Mathematiques (CRM), September 2021. [Jim Luedtke]

- Invited discussant in session “Advances in Statistical Climatology”, Joint Statistical Meetings (Virtual), August 2021. [Michael Stein]