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. I am excited to be joining Google Research in New York City in September 2017!

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. We were recently awarded the INFORMS Computing Society prize for this work. I'm always interested to hear of who's using JuMP, so please get in touch.

Journal articles
  1. I. Dunning, J. Huchette, M. Lubin, JuMP: A modeling language for mathematical optimization. To appear in SIAM Review. (preprint)
  2. J.P. Vielma, I. Dunning, J. Huchette, M. Lubin, Extended Formulations in Mixed Integer Conic Quadratic Programming. Mathematical Programming Computation, 2016. (DOI) (preprint)
  3. M. Lubin, Y. Dvorkin, S. Backhaus, A robust approach to chance constrained optimal power flow with renewable generation, IEEE Transactions on Power Systems, 2016. (DOI) (preprint)
  4. Y. Dvorkin, M. Lubin, S. Backhaus, M. Chertkov, Uncertainty sets for wind power generation, IEEE Transactions on Power Systems, 2016. (DOI) (preprint)
  5. D. Bertsimas, I. Dunning, M. Lubin, Reformulation versus cutting-planes for robust optimization, Computational Management Science, 2016. (DOI) (preprint)
  6. M. Lubin, I. Dunning, Computing in Operations Research using Julia, INFORMS Journal on Computing, 2015. (DOI) (preprint)
  7. 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)
  8. 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)
  9. M. Lubin, K. Martin, C. Petra, B. Sandıkçı, On parallelizing dual decomposition in stochastic integer programming, Operations Research Letters, 2013. (DOI) (preprint)
  10. 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
  11. M. Lubin, C. Petra, 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, I. Zadik, J.P. Vielma, Mixed-integer convex representability, 19th Conference on Integer Programming and Combinatorial Optimization (IPCO), 2017. (preprint)
  2. K. Sundar, H. Nagarajan, M. Lubin, L. Roald, S. Misra, R. Bent, D. Bienstock, Unit Commitment with N-1 Security and Wind Uncertainty, Power Systems Computation Conference (PSCC), 2016. (DOI) (pdf)
  3. M. Lubin, E. Yamangil, R. Bent, J.P. Vielma, Extended Formulations in Mixed-integer Convex Programming, 18th Conference on Integer Programming and Combinatorial Optimization (IPCO), 2016. (DOI) (preprint)
  4. J. Huchette, M. Lubin, C. Petra, Parallel algebraic modeling for stochastic optimization, First Workshop for High Performance Technical Computing in Dynamic Languages (HPTCDL), 2014. (DOI) (preprint)
  5. M. Lubin, C. Petra, M. Anitescu, V. Zavala, Scalable Stochastic Optimization of Complex Energy Systems. International Conference for High Performance Computing, Networking, Storage and Analysis (SC), 2011. (DOI) (pdf)
  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)

  • Mixed-integer convex optimization:
    • Google Research, New York City, November 2016.
    • Scientific Computing Seminar, University of British Columbia, November 2016.
    • SILO Seminar, University of Wisconsin-Madison, October 2016. (video)
    • Department of Computing, Imperial College London, September 2016. (pdf)
    • Zuze Institute Berlin (ZIB), June 2016.
    • Computational Research in Boston and Beyond (CRIBB) Seminar, MIT, April 2016. (pdf)
  • "Voltage-aware chance-constrained optimal power flow and unit commitment". XIV International Conference on Stochastic Programming, Búzios, Brazil, June 2016. (pdf)
  • "Automatic Differentiation Techniques used in JuMP". JuliaCon 2016, Cambridge, MA, June 2016. (youtube) (pdf)
  • "Nonlinear optimization modeling using JuMP and JuliaOpt". American Institute of Chemical Engineers CAST Division, webinar, April 2016. (youtube) (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 University of British Columbia (November 2016), Imperial College London (September 2016), 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