I am a Staff Scientist in the Applied Mathematics and Plasma Physics group (T-5) of the Theoretical Division at Los Alamos National Laboratory and a member of the Advanced Network Science Initiative.
My research develops theory, algorithms, and open-source software for large-scale mixed-integer and nonlinear programming, including global optimization of nonconvex programs, optimization under uncertainty, and learning-accelerated optimization, with applications in power systems, networks and control, and quantum computing.
I have led and co-led externally funded, multidisciplinary research programs with national laboratories and industry partners, and I have mentored 3 postdoctoral researchers and more than 22 graduate researchers. Before my staff appointment, I was a postdoctoral researcher at the Center for Nonlinear Studies at Los Alamos National Laboratory; more about my background is available here.
If you would like to work with talented researchers in a collaborative environment, feel free to contact me.
My research develops mathematical programming formulations and global optimization algorithms for nonconvex programs. Data shape the optimization strategy: abundant, repetitive observations support learning-accelerated methods, while uncertain operating conditions call for stochastic, robust, and adversarial optimization. This methodological core connects applications in power systems, networks and control, and quantum computing.
Across these areas, I focus on formulations and algorithms that retain physical fidelity and mathematical guarantees while remaining useful on realistic systems. The overlaps show where a methods contribution and an application question meet.
Mathematical programming and global optimization algorithms for nonconvex programs form the methodological core. Learning-accelerated strategies and open-source software target repeatedly solved parametric problems, reducing solution times while retaining optimality guarantees. This work also includes optimization under uncertainty for power-system applications.
AC optimal power flow and related discrete variants, including unit commitment and transmission switching, under uncertainty from renewable generation. Resilience work spans microgrid design, reconfiguration, and black-start restoration.
Optimization and spectral graph methods for robust network and topology design, with applications to disturbance propagation, robot localization, and UAV communication networks.
Optimization methods for provably optimal quantum circuit design, efficient hardware-aware encodings for constrained optimization problems, and hybrid quantum–classical approaches that accelerate classical optimization algorithms.