Convex Optimization theory and algorithms with specific interests in:

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

More recently, I have also been actively involved in the following projects,

• First-order methods for large-scale sparse linear programming
• Algorithms for bicriterion traffic assignment
• Methods for Convex/Nonconvex QCQPs and SDPs (with Dr. Weijun Xie and Dr. Santanu Dey)

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, Anton J. Kleywegt, Jacob M. Aguirre. A Low-Rank Augmented Lagrangian Method for Large-Scale Sparse Linear Programming Based on a Hybrid Approach. (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 Aguirre (for 6422). On Complexity of Tropical Ideals and Semidefinite Programming. here.

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