Conference/Workshop Organization


  1. Organizer of the invited section: Convex Optimization in Machine Learning at ICCOPT2013.

Tutorials


  1. Convex Optimization for Big Data
    Presenters: Volkan Cevher (EPFL), Mario Figueiredo (University of Lisbon), Mark Schmidt (University of British Columbia) and Quoc Tran-Dinh (EPFL)
    The IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015.
    Available at: http://lions.epfl.ch/files/content/sites/lions2/files/Presentations/icassp2015_lectures.pdf [pdf]

Talks/Invited Talks


  1. Composite convex minimization involving self-concordant-like cost functions, Modelling, Computation and Optimization in Information Systems and Management Sciences (MCO 2015), May 11-13, 2015, Metz, France.
  2. A Unified Primal-Dual Optimization Framework for Constrained Convex Optimization, invited talk at The 4th IMA Conference on Numerical Linear Algebra and Optimization, September 3-5, 2014, University of Birmingham, England.
  3. Recent Progress in Numerical Methods for Some Classes of Convex Optimization Problems, invited talk at The 2nd International Conference on Computational Science and Engineering (ICCSE-2014), August 19-21, 2014, Ho Chi Minh city, Vietnam.
  4. Composite Convex Minimization: Examples and Solution Methods, invited talk at the Workshop on “Equilibrium and Fixed Point Problems: Theory and Algorithms”, Vietnam Institute for Advanced Study in Mathematics (VIASM), August 25-29, 2014, Hanoi, Vietnam.
  5. Composite Self-concordant Minimization: A Path-Following Method, SIAM Conference on Optimization (SIOPT2014), May 19—22, 2014, San Diego, USA.
  6. Composite Self-Concordant Minimization, SIAM Conference on Imaging Science (SIAM-IS14), May 12—14, 2014, Hong Kong Baptist University, Hong Kong.
  7. Sequential Convex Programming for Nonlinear Optimization with Two Applications in Control, invited talk at Automatic Control Laboratory, EPFL, December 6, 2013, Lausanne, Switzerland.
  8. A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions, The 30th International Conference on Machine Learning (ICML2013), June 16—21, 2013, Atlanta, USA.
  9. Fast decomposition algorithms for large-scale separable convex optimization, invited talk at International Conference on Continuous Optimization (ICCOPT2013), July 27-August 1, 2013, Lisbon, Portugal.
  10. Dual Decomposition Methods for Solving Separable Convex Optimization Problems, invited talk at Laboratory for Information and Inference Systems, EPFL, July 16, 2012, Lausanne, Switzerland.
  11. Structure Exploiting Approaches for Nonlinear Optimization, invited talk at Johann Radon Institute for Computational and Applied Mathematics (RICAM), July 10, 2012, Linz, Austria.
  12. Solving Structural Large Scale Convex Programming Problems via Dual Decomposition and Smoothing Techniques, The 5th International Conference on High Performance Scientific Computing, March 5-9, 2012, Hanoi, Vietnam.
  13. Real-time Sequential Convex Programming for Nonlinear MPC and Application, Conference on Decision and Control (CDC), December 12—15, 2011, Orlando, Florida, USA.
  14. On Sequential Convex Programming, Annual OPTEC meeting, Sol-Cress, Spa, Belgium, 2010.
  15. Adjoint Based Sequential Convex Programming Methods and Applications, The 29th Benelux Meeting on Systems and Control, March 30-April 1 2010, Heeze, The Netherlands.
  16. Real-Time Sequential Convex Programming for Optimal Control Applications, The 4th International Conference on High Performance Scientific Computing, March 2-6, 2009, Hanoi, Vietnam.

Review activity and other services


  1. SIAM J. Optimimization; J. Global Optimization; Automatica; Optimization Methods and Software; IEEE Trans. Automatic Control; IEEE Signal Processing Letters; IEEE Signal Processing Magazine; Computational Optimization and Applications; etc.
  2. SIAM Member, since 2014

http://www.hitwebcounter.com
Visitors (From 20.07.2013)