Me

Miles Lubin

Contact: miles.lubin at gmail.com

I am an Algorithm Developer at Hudson River Trading. Between 2017 and 2022, I was a research scientist in the Algorithms & Optimization team at Google in New York City. I received my Ph.D. from the Operations Research Center at MIT in 2017 advised by Juan Pablo Vielma. I graduated from the University of Chicago in 2011 with a B.S. in Applied Mathematics and M.S. in Statistics. Prior to Google, I spent significant time as a researcher and visitor at Argonne and Los Alamos National Labs. This is my personal research page with links to my publications, talks, and Ph.D. thesis.

I am honored to have received the 2024 Beale—Orchard-Hays Prize, the 2021 Beale—Orchard-Hays Prize, the 2016 INFORMS Computing Society Prize, the 2015 and 2013 COIN-OR Cups, and the COAP 2013 Best Paper Prize.

Journal articles
  1. D. Applegate, M. Díaz, H. Lu, M. Lubin, Infeasibility detection with primal-dual hybrid gradient for large-scale linear programming. SIAM Journal on Optimization, 2024. (DOI) (preprint)
  2. I. Zadik, M. Lubin, J.P. Vielma, Shapes and recession cones in mixed-integer convex representability. Mathematical Programming, 2024. (DOI) (preprint)
  3. M. Lubin, O. Dowson, J.D. Garcia, J. Huchette, B. Legat, J.P. Vielma, JuMP 1.0: Recent improvements to a modeling language for mathematical optimization. Mathematical Programming Computation, 2023. (DOI) (preprint)
  4. D. Applegate, O. Hinder, H. Lu, M. Lubin, Faster First-Order Primal-Dual Methods for Linear Programming using Restarts and Sharpness. Mathematical Programming, 2023. (DOI) (preprint)
  5. B. Legat, O. Dowson, J.D. Garcia, M. Lubin, MathOptInterface: a data structure for mathematical optimization problems. INFORMS Journal on Computing, 2021. (DOI) (preprint)
  6. M. Lubin, I. Zadik, J.P. Vielma, Mixed-integer convex representability. Mathematics of Operations Research, 2021. (DOI) (preprint)
  7. C. Coey, M. Lubin, J.P. Vielma, Outer Approximation With Conic Certificates For Mixed-Integer Convex Problems. Mathematical Programming Computation, 2020. (DOI) (view) (preprint)
  8. M. Lubin, Y. Dvorkin, L. Roald, Chance Constraints for Improving the Security of AC Optimal Power Flow. IEEE Transactions on Power Systems, 2019. (DOI) (preprint)
  9. C. Petra, F. Qiang, M. Lubin, J. Huchette, On efficient Hessian computation using the edge pushing algorithm in Julia. Optimization Methods and Software, 2018. (DOI) (preprint)
  10. M. Lubin, E. Yamangil, R. Bent, J.P. Vielma, Polyhedral approximation in mixed-integer convex optimization. Mathematical Programming Series B, 2017. (DOI) (view) (preprint)
  11. I. Dunning, J. Huchette, M. Lubin, JuMP: A modeling language for mathematical optimization. SIAM Review, 2017. (DOI) (pdf)
  12. J.P. Vielma, I. Dunning, J. Huchette, M. Lubin, Extended Formulations in Mixed Integer Conic Quadratic Programming. Mathematical Programming Computation, 2017. (DOI) (view) (preprint)
  13. 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)
  14. Y. Dvorkin, M. Lubin, S. Backhaus, M. Chertkov, Uncertainty sets for wind power generation, IEEE Transactions on Power Systems, 2016. (DOI) (preprint)
  15. D. Bertsimas, I. Dunning, M. Lubin, Reformulation versus cutting-planes for robust optimization, Computational Management Science, 2016. (DOI) (preprint)
  16. M. Lubin, I. Dunning, Computing in Operations Research using Julia, INFORMS Journal on Computing, 2015. (DOI) (preprint)
  17. 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)
  18. 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)
  19. M. Lubin, K. Martin, C. Petra, B. Sandıkçı, On parallelizing dual decomposition in stochastic integer programming, Operations Research Letters, 2013. (DOI) (preprint)
  20. 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)
  21. 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. D. Applegate, M. Díaz, O. Hinder, H. Lu, M. Lubin, B. O'Donoghue, W. Schudy, Practical Large-Scale Linear Programming using Primal-Dual Hybrid Gradient, Conference on Neural Information Processing Systems (NeurIPS), 2021. (proceedings) (preprint)
  2. N. Doudchenko, K. Khosravi, J. Pouget-Abadie, S. Lahaie, M. Lubin, V. Mirrokni, J. Spiess, G. Imbens, Synthetic Design: An Optimization Approach to Experimental Design with Synthetic Controls, Conference on Neural Information Processing Systems (NeurIPS), 2021. (proceedings)
  3. A. Paliwal, F. Gimeno, V. Nair, Y. Li, M. Lubin, P. Kohli, O. Vinyals, Reinforced Genetic Algorithm Learning for Optimizing Computation Graphs, Eighth International Conference on Learning Representations (ICLR), 2020. (preprint)
  4. C. Coffrin, R. Bent, K. Sundar, Y. Ng, M. Lubin, PowerModels.jl: An Open-Source Framework for Exploring Power Flow Formulations, 20th Power Systems Computation Conference (PSCC), 2018. (DOI) (preprint)
  5. M. Lubin*, I. Zadik*, J.P. Vielma, Mixed-integer convex representability, 19th Conference on Integer Programming and Combinatorial Optimization (IPCO), 2017. (DOI) (preprint)
  6. 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)
  7. 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)
  8. 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)
  9. 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)
* Equal Contribution

Unpublished
  1. J. Revels, M. Lubin, T. Papamarkou, Forward-Mode Automatic Differentiation in Julia. (arXiv)
  2. M. Lubin, D. Bienstock, J.P. Vielma, Two-sided linear chance constraints and extensions. (arXiv)
Talks
  • "What's new in JuMP". ISMP, Montréal, Canada, July 2024. (pdf)
  • "First-order methods for LP". Trends in Computational Discrete Optimization, Providence, RI, April 2023.
  • "Linear programming by first-order methods". West Coast Optimization Meeting, Virtual, May 2021.
  • "The roadmap for JuMP 1.0". JuMP-dev Workshop, Santiago, Chile, March 2019. (youtube) (pdf)
  • "JuMP 0.19 and MathOptInterface: New abstractions for mathematical optimization". ISMP, Bordeaux, France, July 2018.
  • "MathOptInterface and JuMP 0.19". JuMP-dev Workshop, Bordeaux, France, June 2018. (youtube) (pdf)
  • "Mixed-integer convex representability". Mixed Integer Programming Workshop, Greenville, SC, June 2018.
  • "Mixed-integer convex representability" (co-presented with I. Zadik). IPCO 2017, Waterloo, ON, June 2017. (pdf)
  • "The design of JuMP and MathProgBase". JuMP Developers Meetup/Workshop, Cambridge, MA, June 2017. (youtube)
  • "Mixed-integer convex optimization: outer approximation algorithms and modeling power". Ph.D. Thesis Defense, June 2017. (youtube)
  • Mixed-integer convex optimization:
    • IBM Watson, Yorktown Heights, May 2019.
    • GERAD, Montréal, February 2019. (pdf)
    • YinzOR Student Conference, Pittsburgh, August 2018.
    • 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:
    • ROADEF, Lorient, France, February 2018. (youtube)
    • 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 ICCOPT 2022, 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).

Posters
  • "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