Boris Defourny Princeton University Department of Operations Research and Financial Engineering (ORFE) Sherrerd Hall, Charlton St Princeton NJ 08544, USA Tel: +1 609 258 5564 Email: my last name at princeton dot edu
Since April 2011, I am with the Princeton Laboratory for Energy Analysis (PENSA) at the Operations Research and Financial Engineering Department (ORFE) of Princeton University. I work on energy applications at the crossroads of power systems engineering, optimization, and sequential decision making under uncertainty. I received my PhD in December 2010 from the University of Liège, Belgium, for my work in machine learning and sequential decision making under uncertainty. My thesis was focused on multistage stochastic programming techniques. In Liège I was with the Systems and Modeling Research Unit (Systmod), in the Department of Electrical Engineering and Computer Science (Institut Montefiore). My homepage from Montefiore can be accessed here.

Submitted

• Optimal Information Blending with Measurements in the L2 sphere.
  B. Defourny, I.O. Ryzhov and W.B. Powell. Submitted to Mathematics of Operations Research.
• A Robust Solution to the Load Curtailment Problem.
  H.P. Simao, H.B. Jeong, B. Defourny, W.B. Powell, A. Boulanger, A. Gagneja, L. Wu and R.N. Anderson. Submitted to IEEE Trans. Smart Grid, Special issue on Optimization Methods and Algorithms applied to Smart Grid.

Recent

• Robust Forecasting for Unit Commitment with Wind.
  B. Defourny, H.P. Simao and W.B. Powell. Proceedings of the 46th Hawaii International Conference on System Sciences (HICSS-46), January 2013.
• Ranking and selection meets robust optimization.
  I.O. Ryzhov, B. Defourny and W.B. Powell. Proceedings of the 2012 Winter Simulation Conference, December 2012.
• Scenario Trees and Policy Selection for Multistage Stochastic Programming Using Machine Learning.
  B. Defourny, D. Ernst and L. Wehenkel.
  INFORMS Journal on Computing, ijoc.1120.0516. Available as Article in Advance.

Past

• Machine Learning Solution Methods for Multistage Stochastic Programming.
  B. Defourny. Ph.D. thesis, University of Liège, 2010. Abstract.
• Multistage Stochastic Programming: A Scenario Tree Based Approach to Planning under Uncertainty.
  B. Defourny, D. Ernst and L. Wehenkel.
  In L.E. Sucar, E.F. Morales, and J. Hoey (ed.), Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions, pp. 97-143, IGI Global, 2012.
• Large Margin Classification with the Progressive Hedging Algorithm.
  B. Defourny and L. Wehenkel.
  OPT 2009: Second NIPS Workshop on Optimization for Machine Learning, Whistler, Canada, 2009.
• Bounds for Multistage Stochastic Programs using Supervised Learning Strategies.
  B. Defourny, D. Ernst and L. Wehenkel.
  Stochastic Algorithms: Foundations and Applications. Fifth International Symposium, SAGA 2009. Lecture Notes in Computer Science, vol. 5792, pp. 61-73, Springer, 2009.
  Postprint by Springer. Preprint.
  
• Supervised Learning of Intra-Daily Recourse Strategies for Generation Management Under Uncertainties.
  B. Cornelusse, G. Vignal, B. Defourny and L. Wehenkel.
  2009 IEEE Bucharest PowerTech.
• Probability Density Estimation by Perturbing and Combining Tree Structured Markov Networks.
  S. Ammar, P. Leray, B. Defourny and L. Wehenkel.
  Symbolic and Quantitative Approaches to Reasoning with Uncertainty - 10th ECSQARU, Lecture Notes in Artificial Intelligence, vol. 5590, pp. 156-167, Springer, 2009.
• Planning under Uncertainty, Ensembles of Disturbance Trees and Kernelized Discrete Action Spaces.
  B. Defourny, D. Ernst and L. Wehenkel.
  Proceedings of the IEEE International Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL 2009), pp. 145-152, Nashville TN, 2009.
• Risk-Aware Decision Making and Dynamic Programming.
  B. Defourny, D. Ernst and L. Wehenkel.
  Selected for oral presentation at the NIPS-08 Workshop on Model Uncertainty and Risk in Reinforcement Learning, Whistler, Canada, 2008.
• Lazy Planning under Uncertainty by Optimizing Decisions on an Ensemble of Incomplete Disturbance Trees.
  B. Defourny, D. Ernst and L. Wehenkel.
  In Recent Advances in Reinforcement Learning, 8th European Workshop, EWRL'08, Lecture Notes in Artificial Intelligence, vol. 5323, pp. 1-14, Springer, 2008.
  Postprint by Springer. Preprint.
• High-Dimensional Probability Density Estimation with Randomized Ensembles of Tree Structured Bayesian Networks.
  S. Ammar, P. Leray, B. Defourny and L. Wehenkel.
  In Proceedings of the 4th European Workshop on Probabilistic Graphical Models (PGM 2008), Hirtshals, Denmark, 2008.
• Projecting Generation Decisions Induced by a Stochastic Program on a Family of Supply Curve Functions.
  B. Defourny and L. Wehenkel.
  In 3rd Carnegie Mellon Conference on the Electricity Industry, Pittsburgh PA, 2007.