I am a Ph.D. candidate in Operations Research at MIT. I was previously in the Mathematics and Computer Science (MCS) division at Argonne National Laboratory. My interests include Optimization, Probability and Statistics, and High Performance Computing. My advisor is Juan Pablo Vielma.
- M.S. Statistics, University of Chicago, 2011
- B.S. Applied Mathematics, University of Chicago, 2011
- D. Bertsimas, I. Dunning, and M. Lubin, "Reformulation versus cutting-planes for robust optimization", to appear in Computational Management Science, 2015. DOI Preprint
- M. Lubin and I. Dunning, "Computing in Operations Research using Julia". INFORMS Journal on Computing 27(2), pages 238-248, 2015. DOI arXiv preprint
- I. Dunning, V. Gupta, A. King, J. Kung, M. Lubin, and J. Silberholz, "A course on advanced software tools for Operations Research and Analytics". INFORMS Transactions on Education 15(2), pages 169-179, 2015. DOI Preprint
- C. Petra, O. Schenk, M. Lubin, and K. Gärtner, "An augmented incomplete factorization approach for computing the Schur complement in stochastic optimization". SIAM J. Scientific Computing, 36(2), pages C139-C162, 2014. DOI Preprint
- M. Lubin, K. Martin, C. Petra, and B. Sandıkçı, "On parallelizing dual decomposition in stochastic integer programming". Operations Research Letters 41(3), pages 252-258, 2013. DOI Preprint
- M. Lubin, J. A. J. Hall, C. Petra, and M. Anitescu, "Parallel distributed-memory simplex for large-scale stochastic LP problems". Computational Optimization and Applications 55(3), pages 571-596, 2013. DOI Preprint COAP 2013 Best Paper & COIN-OR 2013 Cup winner
- M. Lubin, C. Petra, and M. Anitescu, "The parallel solution of dense saddle-point linear systems arising in stochastic programming". Optimization Methods and Software 27(4-5), pages 845-864, 2012. DOI Preprint
- J. Huchette, M. Lubin, and C. Petra, "Parallel algebraic modeling for stochastic optimization". Proceedings of HPTCDL '14, pages 29-35. IEEE Press. DOI Preprint
- M. Lubin, C. Petra, M. Anitescu, and V. Zavala, "Scalable Stochastic Optimization of Complex Energy Systems". In Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC '11), pages 64:1-64:10. ACM. Public download
Articles under review
- I. Dunning, J. Huchette, and M. Lubin, "JuMP: A modeling language for mathematical optimization". Preprint
- M. Lubin, Y. Dvorkin, S. Backhaus, "A robust approach to chance constrained optimial power flow with renewable generation". Preprint
- J. Vielma, I. Dunning, J. Huchette, and M. Lubin, "Extended Formulations in Mixed Integer Conic Quadratic Programming". Preprint
- (co-presented with J. Huchette) OR seminar, Carnegie Mellon University, March 2015.
- SIAM CSE 2015, Salt Lake City, UT, March 2015.
- INFORMS 2014, San Francisco, CA, November 2014. PDF
- APMOD 2014, University of Warwick, UK, April 2014.
- "Computing in Operations Research using Julia" (co-presented with I. Dunning). INFORMS 2013, Minneapolis, MN, October, 2013. PDF
- "Parallel and distributed solution methods for two-stage stochastic (MI)LPs". 21st International Symposium on Mathematical Programming (ISMP 2012), Berlin, Germany, August 2012.
- "Parallel linear-algebra decomposition methods in stochastic optimization". 7th International Workshop on Parallel Matrix Algorithms and Applications (PMAA 2012), Birkbeck University of London, UK, June 2012.
- "Parallel distributed-memory simplex for large-scale stochastic LP problems". Edinburgh Research Group in Optimization (ERGO) seminar, University of Edinburgh, UK, June 2012. PDF
- "Scalable Stochastic Optimization of Complex Energy Systems". 2011 International Conference for High Performance Computing, Networking, Storage, and Analysis (SC '11), Seattle, WA, November 2011.
- "JuMP: open-source algebraic modeling in Julia" (co-authors I. Dunning and J. Huchette). 11th Mixed-Integer Programming Workshop, Ohio State University, July 2014. Honorable Mention, Best Poster Award
- JuMP - A mathematical programming modeling language in Julia.
- PIPS-IPM - Parallel interior-point solver for large-scale block-angular (stochastic) LPs and QPs.
- PIPS-S - Parallel sparsity-exploiting revised simplex solver for large-scale block-angular (stochastic) LPs. Contact if interested.
- Previously spent a semester abroad in Córdoba, Argentina, at FaMAF (Universidad Nacional de Córdoba).
- NY Times article from my high school days.
- UChicago Sailing Club
Email: mlubin mit.edu, replace blank space with @.