Miles Lubin

Contact: mlubin at

I am a fourth-year Ph.D. candidate in Operations Research at MIT advised by Juan Pablo Vielma. I received my B.S. in Applied Mathematics and M.S. in Statistics from the University of Chicago in 2011. After graduating, I spent a year as a researcher at Argonne National Laboratory before starting at MIT.

My research interests span diverse areas of mathematical optimization, with a unifying theme of developing new methodologies for large-scale optimization drawing from motivating applications in renewable energy. I have published work in chance constrained optimization, mixed-integer conic optimization, robust optimization, stochastic programming, algebraic modeling, automatic differentiation, numerical linear algebra, and parallel computing techniques for large-scale problems.

In 2012, Iain Dunning and I (later joined by Joey Huchette) started developing JuMP, an open-source algebraic modeling language for optimization. Since then, JuMP has been used for teaching in at least 8 universities and by numerous researchers and companies worldwide. I am a co-founder of the JuliaOpt organization which has brought together early adopters in academia and industry with the goal of developing high quality open-source software for optimization in Julia. I'm always interested to hear of who's using JuMP, especially constructive feedback, so please get in touch.

Journal articles
  1. Y. Dvorkin, M. Lubin, S. Backhaus, M. Chertkov, Uncertainty sets for wind power generation, IEEE Transactions on Power Systems, 2015. (DOI) (preprint)
  2. D. Bertsimas, I. Dunning, M. Lubin, Reformulation versus cutting-planes for robust optimization, Computational Management Science, 2015. (DOI) (preprint)
  3. M. Lubin, I. Dunning, Computing in Operations Research using Julia, INFORMS Journal on Computing, 2015. (DOI) (preprint) Third most downloaded paper in IJOC this year
  4. I. Dunning, V. Gupta, A. King, J. Kung, M. Lubin, J. Silberholz, A course on advanced software tools for Operations Research and Analytics, INFORMS Transactions on Education, 2015. (DOI) (pdf)
  5. C. Petra, O. Schenk, M. Lubin, K. Gärtner, An augmented incomplete factorization approach for computing the Schur complement in stochastic optimization, SIAM Journal on Scientific Computing, 2014. (DOI) (preprint)
  6. M. Lubin, K. Martin, C. Petra, B. Sandıkçı, On parallelizing dual decomposition in stochastic integer programming, Operations Research Letters, 2013. (DOI) (preprint)
  7. M. Lubin, J. A. J. Hall, C. Petra, M. Anitescu, Parallel distributed-memory simplex for large-scale stochastic LP problems, Computational Optimization and Applications, 2013. (DOI) (preprint) COAP 2013 Best Paper & COIN-OR 2013 Cup winner
  8. M. Lubin, C. Petra, and M. Anitescu, The parallel solution of dense saddle-point linear systems arising in stochastic programming, Optimization Methods and Software, 2012. (DOI) (preprint)
Conference papers
  1. J. Huchette, M. Lubin, and C. Petra, Parallel algebraic modeling for stochastic optimization, Proceedings of HPTCDL '14, 2014. (DOI) (preprint)
  2. 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), 2011. (DOI) (pdf)
Under review
  1. I. Dunning, J. Huchette, M. Lubin, JuMP: A modeling language for mathematical optimization. (preprint)
  2. M. Lubin, Y. Dvorkin, S. Backhaus, A robust approach to chance constrained optimal power flow with renewable generation. (preprint)
  3. J.P. Vielma, I. Dunning, J. Huchette, M. Lubin, Extended Formulations in Mixed Integer Conic Quadratic Programming. (preprint)
Working papers
  1. M. Lubin, D. Bienstock, J.P. Vielma, Two-sided linear chance constraints and extensions. (arXiv)
  2. M. Lubin, E. Yamangil, R. Bent, Mixed-integer disciplined convex programming.

  • "Automatic differentiation in Julia" (co-presented with J. Revels). 17th EURO AD Workshop, Argonne, IL, August 2015. (materials)
  • "Abstract glue for optimization in Julia". 22nd International Symposium on Mathematical Programming (ISMP 2015), Pittsburgh, PA, July 2015. (pdf)
  • JuMP:
    • (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.

In addition to the above academic talks, I have given JuMP tutorials, often together with fellow developers, at Montreal Optimization Days 2016 (upcoming), JuliaCon 2015 (June 2015), MIT Energy Initiative (April 2015), Carnegie Mellon University (March 2015), Grid Science Winter School (January 2015), UC Berkeley (November 2014), Universidad Adolfo Ibañez (January 2014, in Spanish), and MIT Operations Research Center (October 2013).

  • "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