(Major) Convex Optimization theory and algorithms with specific interests in:

• Investigating complexity and developing efficient algorithms for Stochastic and Nonlinear Programming
• Distributionally Robust Optimization
• Statistical Inference via Convex Optimization

More recently, I've also taken a keen interest on the following:

• Convex-Concave Saddle Point Problems
• First-order methods for Large-scale linear programs

Çaglar Çaglayan, Yunan Liu, Turgay Ayer, et al. Physician staffing in emergency rooms (ERs): Opening the black-box of ER care via a multi-class multi-stage network. (Under revision at Management Science.)

Renato D.C. Monteiro, Jacob M. Aguirre, Anton J. Kleywegt. A Primal-Dual Hybrid Frank-Wolfe Method for Large-Scale Linear Programs. (Work in progress)

Xinchao Liu, Jacob M. Aguirre, Xiao Liu. Optimal Compressed Sensing Dynamic Mode Decomposition for In-Situ Large-scale Dataset.* (Under revisions)

Jacob M. Aguirre, Renato D.C. Monteiro, Anton J. Kleywegt. Accelerated Inexact High-order Proximal-Point methods for Convex Tensor Optimization. (Work in progress)

Renato D.C. Monteiro and Jacob M. Aguirre. A Low-Rank Hybrid Augmented Lagrangian Method for Maximum Stable Set. (Work in progress)

Jacob M. Aguirre. On Complexity of Tropical Ideals and Generic Semidefinite Program. Preprint here.

Jacob Aguirre, Shrey Patel, Ruirui Ma. A Review of High Dimensional Nonlinear Dimension Reduction Methods Undergrad course paper