Research
My research centers on continuous optimization, with an emphasis on complexity analysis and scalable algorithms for large linear, conic, and nonlinear models. I am advised by Anton J. Kleywegt and Renato D.C. Monteiro, and I work closely with Arkadi Nemirovski, Dmitrii M. Ostrovskii, Diego Cifuentes, Vincent Guigues, Victor Hugo Nascimento, and Arnesh Sujanani.
People and Venues
Advisors and collaborators
My primary advising relationship is with Anton J. Kleywegt and Renato D.C. Monteiro. I also collaborate on recent projects with Arkadi Nemirovski, Dmitrii M. Ostrovskii, Diego Cifuentes, Vincent Guigues, Victor Hugo Nascimento, and Arnesh Sujanani.
Submission venues
Current Directions
Major direction
I develop efficient algorithms for large-scale linear programming, convex quadratic programming, semidefinite programming, complementarity problems, variational inequalities, nonlinear convex programming, and continuous relaxations of combinatorial optimization problems.
Parallel direction
I also work on fast numerical methods and software for large optimization problems, with particular interest in low-rank structure and GPU-accelerated first-order solvers.
Selected Work
The link order is consistent across entries: paper, code, and supporting files.
Submitted to Mathematical Programming Computation
GPU-accelerated low-rank methods for semidefinite programs with large, structured instances.
Working paper
Hybrid first-order and low-rank ideas for linear programs where scale and structure both matter.
Submitted to Operations Research
Continuous optimization methods for bicriterion traffic assignment, together with large transportation benchmark instances.
Submitted to Mathematics of Operations Research
Complexity guarantees for accelerated smoothing methods in large-scale convex optimization.