Miles Lubin

Contact: mlubin at

I am a fifth-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 10 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, so please get in touch.

Journal articles
  1. M. Lubin, Y. Dvorkin, S. Backhaus, A robust approach to chance constrained optimal power flow with renewable generation, IEEE Transactions on Power Systems, 2015. (DOI) (preprint)
  2. Y. Dvorkin, M. Lubin, S. Backhaus, M. Chertkov, Uncertainty sets for wind power generation, IEEE Transactions on Power Systems, 2015. (DOI) (preprint)
  3. D. Bertsimas, I. Dunning, M. Lubin, Reformulation versus cutting-planes for robust optimization, Computational Management Science, 2015. (DOI) (preprint)
  4. M. Lubin, I. Dunning, Computing in Operations Research using Julia, INFORMS Journal on Computing, 2015. (DOI) (preprint)
  5. 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)
  6. 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)
  7. M. Lubin, K. Martin, C. Petra, B. Sandıkçı, On parallelizing dual decomposition in stochastic integer programming, Operations Research Letters, 2013. (DOI) (preprint)
  8. 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
  9. 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. M. Lubin, E. Yamangil, R. Bent, J.P. Vielma, Extended Formulations in Mixed-integer Convex Programming, Proceedings of IPCO 2016, 2016. (DOI) (preprint)
  2. J. Huchette, M. Lubin, and C. Petra, Parallel algebraic modeling for stochastic optimization, Proceedings of HPTCDL '14, 2014. (DOI) (preprint)
  3. 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. M. Lubin, E. Yamangil, R. Bent, J.P. Vielma, Polyhedral approximation in mixed-integer convex optimization. (preprint)
  2. M. Lubin, D. Bienstock, J.P. Vielma, Two-sided linear chance constraints and extensions. (preprint)
  3. K. Sundar, H. Nagarajan, M. Lubin, L. Roald, S. Misra, R. Bent, D. Bienstock, Unit Commitment with N-1 Security and Wind Uncertainty. (preprint)
  4. I. Dunning, J. Huchette, M. Lubin, JuMP: A modeling language for mathematical optimization. (preprint)
  5. J.P. Vielma, I. Dunning, J. Huchette, M. Lubin, Extended Formulations in Mixed Integer Conic Quadratic Programming. (preprint)

  • "Automatic Differentiation Techniques used in JuMP". JuliaCon 2016, Cambridge, MA, June 2016. (youtube) (pdf)
  • "Extended formulations in mixed-integer convex programming". Zuze Institute Berlin (ZIB), June 2016.
  • "Nonlinear optimization modeling using JuMP and JuliaOpt". American Institute of Chemical Engineers CAST Division, webinar, April 2016. (youtube) (pdf)
  • "Mixed-integer convex optimization". Computational Research in Boston and Beyond (CRIBB) Seminar, MIT, April 2016. (pdf)
  • "Mixed-integer disciplined convex programming". Linear Algebra and Optimization Seminar, ICME, Stanford University, January 2016. (pdf)
  • "Convexity and approximation of nonlinear Gaussian chance constraints". INFORMS 2015, Philadelphia, PA, November 2015. (pdf)
  • "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:
    • Applied Mathematics seminar, University of California, Merced, January, 2016.
    • (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 Universidade Federal do Paraná (July 2016, in mostly Portuguese), Optimization Days 2016 (May 2016), 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).

  • "Extended Formulations in Mixed-integer Convex Programming" (co-authors E. Yamangil, R. Bent, and J.P. Vielma). 13th Mixed Integer Programming Workshop, University of Miami, May 2016.
  • "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