2019

  • ICERM “Mathematical Optimization of Systems Impacted by Rare, High-Impact Random Events” Workshop, Brown University, June 2019.
    • “Rare Event Simulation in Energy Systems” [Mihai Anitescu]
    • “A Multistage Distributionally Robust Approach to Water Allocation under Climate Uncertainty” [Gu¨zin Bayraksan]
    • “Large Deviation Theory for the Analysis of Power Transmission Systems Subject to Stochastic Forcing” [David Barajas-Solano]
    • “A Lagrangian Dual Approach for Identifying the Worst Contingencies in Power Systems” [Kibaek Kim]
    • “Chance-Constrained AC Optimal Power Flow: Modelling and Solution Approaches”
    • [Line Roald]
    • “Scalable Solution of Chance-Constrained Nonlinear Programs” [Victor Zavala]
  • 15th International Conference on Stochastic programming, Trondheihm, Norway, July, 2019.
    • Plenary “Effective Scenarios in Distributionally Robust and Risk-Averse Stochastic Programming” [Gu¨zin Bayraksan]
    • “Branching Strategies on Decomposition Methods for Mixed-Integer Programming”
    • [Kibaek Kim]
    • “Parallelizing Subgradient Methods For The Lagrangian Dual In Stochastic Mixed- Integer Programming” [Jeff Linderoth]
    • “Solving chance-constrained problems via a smooth sample-based nonlinear approx- imation” [Jim Luedtke]
    • “Chance-Constrained AC Optimal Power Flow” [Line Roald]
    • “A Sigmoidal Approximation For Chance-Constrained Nonlinear Programs” [Victor Zavala]
  • SIAM Conference on Computational Science and Engineering (SIAM CSE19), Spokane, WA, February 2019.
    • Keynote “Modelling 100 percent renewable electricity” [Michael Ferris]
    • “Data Assimilation Subject to Practical Imperfections” [Zhenyu Huang]
    • “Statistical Learning for Power Systems Optimization: An Active Set Approach”
    • [Line Roald]
    • “A Scalable Global Optimization Algorithm for Stochastic Nonlinear Programs” [Victor Zavala]
  • “Space-time statistics”, Data Science in Engineering Conference, Madison, August 2019.
  • [Mihai Anitescu]
  • Uncertainty Quantification in Energy Systems”, IEEE-PES General Meeting, “Compu- tation in Uncertainty” panel, August 4, 2019 [Mihai Anitescu]
  • “Multistage Distributionally Robust Optimization with Total Variation Distance: Mod- eling and Effective Scenarios,” Systems, Information, Learning, and Optimization (SILO) Seminar Series, the University of Wisconsin at Madison, May 2019.  [Gu¨zin Bayraksan]
  • “Effective Scenarios in Multistage Distributionally Robust Optimization,” Inaugural East Coast Optimization Meeting (ECOM) 2019, Fairfax, Virginia, April 2019.  [Gu¨zin Bayrak- san]
  • “Effective Scenarios in Multistage Distributionally Robust Optimization with Total Vari- ation Distance,” Graduate Program in Operations Research and Industrial Engineering Seminar Series, University of Texas at Austin, February 2019.  [Gu¨zin Bayraksan]
  • “Effective Scenarios in Multistage Distributionally Robust Optimization,” BIRS Work- shop on Models and Algorithms for Sequential Decision Problems Under Uncertainty, Banff, Alberta, Canada, January 2019.  [Gu¨zin Bayraksan]
  • “Capacity expansion and operations tradeoffs in renewable electricity”, International Con- ference on Continuous Optimization (ICCOPT), Berlin, August 2019. [Michael Ferris]
  • “Computation in Markets with Risk”, Industrial and System Engineering, Georgia Tech, May 27, 2019; BIRS-CMO, Oaxaca, September 2019. [Michael Ferris]
  • Keynote “Computation in Markets with Risk”, Open for Business Meeting, Isaac Newton Institute, Cambridge, England, May 2019. [Michael Ferris]
  • “Modelling 100 percent renewable electricity”, MES Workshop, Isaac Newton Institute, Cambridge, England, March 2019. [Michael Ferris]
  • “Planning a 100% renewable electricity system”, The Cleantech Forum, Wisconsin Tech- nology Council, Madison, February 2019. [Michael Ferris]
  • “Data Assimilation Subject to Practical Imperfections”, Workshop on Distribution and Transmission System Monitoring. Boston, MA. May 2019. [Zhenyu Huang]
  • “PNNL’s Support to AGM Program: Gaining Insights through Analytics”, 2019 AGM Peer Review. Lemont, Il. July 2019. [Zhenyu Huang]
  • Perspectives on Integer Programming for Sparse Optimization”, 23rd Combinatorial Op- timization Workshop, Aussois, France, January 2019. [Jeff Linderoth]
  • “Perspectives on Integer Programming for Sparse Optimization”, Mitsubishi Electric Re- search Lab, July 2019. [Jeff Linderoth]
  • “Perspectives on Integer Programming for Sparse Optimization”, University of California- Berkeley, March 2019. [Jeff Linderoth]
  • “Intersection disjunctions for reverse convex sets”, 23rd Aussois Combinatorial Optimiza- tion Workshop, Aussois, France, January 2019. [Jim Luedtke]
  • “Managing Uncertainty in Electric Power Systems using Chance Constrained Optimiza- tion and Statistical Learning”, Texas A&M Power Seminar, College Station, TX, April 2019. [Line Roald]
  • “On active constraints in Optimal Power Flow problems – Learning optimal solutions  & identifying important constraints”, NREL Workshop on Innovative Optimization and Control Methods for Highly Distributed Autonomous Systems, Golden, CO, April 2019. [Line Roald]
  • “Electric Power To The People! Power Systems Optimization in the Age of Renewable Energy”, LANL Grid Science Winter School, Santa Fe, NM, January 2019. [Line Roald]
  • “Uncertainty Quantification in Various Applications”, Joint Statistical Meetings, Denver, July 2019. [Michael Stein]
  • “Statistical extremes: Theory and practice”, Haverford College, March 2019. [Michael Stein]
  • “Statistical extremes: Theory and practice”, Rutgers Statistics Symposium, May 2019.
  • [Michael Stein]
  • “Statistical analysis of large initial condition climate ensembles”, International Union of Geodesy and Geophysics, Montreal, July 2019. [Michael Stein]
  • Keynote “Some Thoughts on Gaussian Processes for Emulation of Deterministic Com- puter Models”, Workshop on Effective and efficient Gaussian processes, Turing Institute, London, August 2019. [Michael Stein]
  • “The right model for each scale: From singularity tracking to predictive machine learning”, University of Washington SIAM Chapter. Seattle, WA. April 2019. [Panos Stinis]
  • “Physics-Informed Deep Neural Networks for Learning Dynamics of Complex Systems”, SSEC Symposium, 52nd Hawaii International Conference on System Sciences, HICSS 2019, Grand Wailea, Maui, Hawaii, USA, January 2019. [Alexandre Tartakovsky]
  • Department of Combinatorics and Optimization, University of Waterloo, colloquium, March 2019. [Steve Wright]
  • Department of Industrial and Systems Engineering, Texas A&M, distinguished seminar, April 2019. [Steve Wright]
  • Computational Science and Engineering Symposium, University of Toronto, May 2019. [Steve Wright]
    • SIAM Conference on Computational Science and Engineering (SIAM CSE19), Spokane, WA, February 2019.“An Optimal Experimental Design Framework for Adaptive Inflation and Covariance Localization for Ensemble Filter” [Ahmed Attia]
    • “Approximate Bayesian model inversion for PDEs with unknown parameters and constitutive relations” [David Barajas-Solano]
    • “Assimilating Data in Models with Stochastic Parameters” [Emil Constantinescu]
    • “Renormalized Reduced Order Models for Long Term Prediction” [Panos Stinis]
  • “Tuning Covariance Localization using Machine Learning” Machine Learning and Data Assimilation for Dynamical Systems (MLDADS). The International Conference on Computational Science (ICCS), Faro, Portugal, June 2019. [Ahmed Attia]
  • “A Scalable Decomposition Approach for Stochastic Mixed-Integer Programs, UG Work- shop 2019”, Berlin, Germany, January 2019. [Kibaek Kim]
  • “Physics-informed machine learning method for forecasting and uncertainty quantification of partially observed and unobserved states in power grids,” 52nd Hawaii International Conference on System Sciences, HICSS 2019, Grand Wailea, Maui, Hawaii, USA, January 2019. [Alexandre Tartakovsky]