(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

(Minor) Some practical and real-world application areas I've been working in are:

• Entity Ranking and Statistical Preference Learning
• Revenue Management

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 perspective on Frank-Wolfe decompositions for low rank matrix decompositions. (Work in progress, Aug. 23).

Anton J. Kleywegt, Jacob M. Aguirre, Renato D.C. Monteiro. On Joint Assortment and Pricing Optimization under Mixtures of Multinomial logits: Hardness and approximations. (Work in progress, Aug. 23).

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

Jacob Aguirre, Shrey Patel, Ruirui Ma. A Review of High Dimensional Nonlinear Dimension Reduction Methods Paper here