Joey Huchette



  • Strong convex relaxations and mixed-integer programming formulations for trained neural networks. [arXiv]
    With Ross Anderson, Christian Tjandraatmadja, and Juan Pablo Vielma.
    An extended abstract version, focusing on MIP formulations for ReLU-based networks: [arXiv]
  • A geometric way to build mixed-integer programming formulations. [arXiv]
    With Juan Pablo Vielma.
  • Nonconvex piecewise linear functions: Advanced formulations and simple modeling tools. [arXiv]
    With Juan Pablo Vielma.
  • A mixed-integer branching approach for very small formulations of disjunctive constraints. [arXiv]
    With Juan Pablo Vielma.


  • A combinatorial approach for small and strong formulations of disjunctive constraints. [arXiv]
    With Juan Pablo Vielma.
    Mathematics of Operations Research, forthcoming, 2018.
    Second place in the 2018 INFORMS Optimization Society Student Paper Prize.
  • On efficient Hessian computation using the edge pushing algorithm in Julia. [pdf]
    With Cosmin Petra, Feng Qiang, and Miles Lubin.
    Optimization Methods and Software, forthcoming, 2018.
  • JuMP: A modeling language for mathematical optimization. [arXiv]
    With Iain Dunning and Miles Lubin.
    SIAM Review, 2017.
    Winner of the 2016 INFORMS Computing Society Prize.
    Winner of the 2016 MIT Operations Research Center Best Student Paper Award.
    Winner of the 2015 COIN-OR INFORMS Cup.
  • Extended formulations in mixed integer conic quadratic programming. [arXiv]
    With Juan Pablo Vielma, Iain Dunning, and Miles Lubin.
    Mathematical Programming Computation, 2017.
  • Beating the SDP bound for the floor layout problem: A simple combinatorial idea. [arXiv]
    With Santanu Dey and Juan Pablo Vielma.
    INFOR: Information Systems and Operational Research, forthcoming, 2017.
  • Strong mixed-integer formulations for the floor layout problem. [arXiv]
    With Santanu Dey and Juan Pablo Vielma.
    INFOR: Information Systems and Operational Research, forthcoming, 2017.
  • Parallel algebraic modeling for stochastic optimization. [ACM]
    With Miles Lubin and Cosmin Petra.
    In Proceedings of HPTCDL 2014.
  • Taming parallel I/O complexity with auto-tuning. [ACM]
    With Babak Behzad, Huong Luu, Surendra Byna, Prabhat, Ruth Aydt, Quincey Koziol, and Marc Snir.
    In Proceedings of SC 2013.


  • Advanced mixed-integer programming formulations: Methodology, computation, and application. [pdf]

About Me

  • I am a postdoctoral researcher in the Operations Research group at Google Research (Cambridge office).
  • In Summer 2019, I will be joining the Computational and Applied Mathematics department at Rice University as an assistant professor.
  • I graduated with:
    • a PhD from the Operations Research Center at MIT (2018).
    • a B.A. from Rice University (2013).
  • I've also spent time at Akamai Technologies, Argonne National Laboratory, and Lawrence Berkeley National Laboratory.

Upcoming travel: INFORMS Computing, JuMP developers workshop, MIP 2019.

CV (Updated 9/22/2018).

Email: joehuchette -at- gmail -dot- com.


Google Scholar.

Research Interests

My primary interests are in the use of mathematical optimization to solve difficult decision problems. Much of my current work is in the area of integer optimization. I am also interested in computational technology, and particularly user-facing software tools for modeling and decision-making.