- Small independent branching formulations for unions of V-polyhedra. [arXiv]
- Beating the SDP bound for the floor layout problem: A simple combinatorial idea. [arXiv]
- Strong mixed-integer formulations for the floor layout problem. [arXiv]
- JuMP: A modeling language for mathematical optimization. [arXiv]
To appear in SIAM Review, 2016.
- Extended formulations in mixed integer conic quadratic programming. [arXiv]
To appear in Mathematical Programming Computation, 2016.
- Parallel algebraic modeling for stochastic optimization. [ACM]
In Proceedings of HPTCDL 2014.
- Taming parallel I/O complexity with auto-tuning. [ACM]
In Proceedings of SC 2013.
- A framework for auto-tuning HDF5 applications. [ACM]
In Proceedings of HPDC 2013.
- JuliaOpt - a suite of optimization software in Julia. Includes:
- JuMP: an algebraic modeling language for linear, integer, and nonlinear optimization.
- Convex.jl: a "disciplined convex programming" modeling language.
- Efficient wrappers for over a dozen state-of-the-art solvers (Gurobi, Ipopt, Mosek, etc.), with a unified, abstract interface.
- Small independent branching formulations for unions of V-polyhedra
- New mixed-integer approaches to the floor layout problem
- INFORMS 2015
- Argonne National Laboratory (2015)
- ISMP 2015
- MIP 2015 (poster)
- INFORMS 2014
- MIP 2014 (poster)
- Modeling optimization problems with JuMP in Julia
- Carnegie Mellon (2014, joint with Miles Lubin)
- Georgia Tech (2014)
- Berkeley (2014, joint with Iain Dunning and Miles Lubin)
- JuliaOpt - Optimization packages for Julia
- JuliaCon 2015 (workshop, joint with Iain Dunning, Miles Lubin, and Madeleine Udell)
- JuliaCon 2014 (joint with Iain Dunning)
- Teaching assistant for MIT 15.083J: Integer Programming and Combinatorial Optimization (Spring 2016).
- Organized and co-taught two sessions of MIT 15.S60: Software Tools for Operations Research. Course materials: 2015 and 2016.
- Co-taught a total of 6 recitations on JuMP for MIT 15.058, 15.081J, and 15.085J (2014).
I study operations research, specifically the theory and application of optimization. Much of my current work is concerned with mathematical formulations: that is, how to translate a high-level optimization problem to a mathematical description we can solve efficiently.
I'm also interested in all aspects of computational optimization, especially user-facing tools for modeling and for developing advanced algorithms.
I'm a PhD student in the Operations Research Center at MIT, advised by Juan Pablo Vielma. I'm supported by the NSF Graduate Fellowship. I received my B.A. in Applied Mathematics from Rice University, where I worked with Beatrice Riviere and Hadley Wickham.
CV (Updated 3/2/2016).