Optimization: Models, Theory and Algorithms: Largescale linear optimization; Mixedinteger and conic programming; Combinatorial optimization; Polyhedral theory: Nonlinear optimization; Dynamic programming; Theory, Algorithms and Computational methods for Mixed Integer Linear and Nonlinear Optimization.
Stochastic Models: Queuing Theory; Queuing models; Resource sharing; Parameter optimization; Performance Analysis; Polling systems; Applications in wireless communication
Stochastic Control: Stochastic dynamic programming (MDPs); Sensitivity analysis; Reinforcement learning; Diffusion equations; Viscosity solutions; Optimal control; Stochastic Approximation.
Simulation Modeling and Analysis: Discreteevent simulation; System dynamics methodology; Hybrid (discretecontinuous) modeling; Distributed and parallel simulations; Statistical data analysis; Simulation optimization: Multilevel Monte Carlo methods.
Game Theory: Mechanism design: Dynamic and stochastic games; Deterministic and stochastic differential games: Games with stopping; Approximate equilibria; Auctions; Network games and learning.
Artificial intelligencebased methods: Search methods; Metaheuristics; Neural networks; Model predictivecontrol.
Logistics, Inventory and Transportation: Transport operations planning (road, rail, air, and sea); Network design; Capacity planning; Operations scheduling and routing; Fleet and crew planning and rostering; Timetabling and rake allocation of rail services
Supply Chain Analysis: Information sharing; Coordination; Contract analysis and design; Realtime decision making; Performance and stability analysis; Reverse logistics and closed loop supply chains; Decisionmaking under uncertainties; Quality of service.
Financial Engineering: Mathematical finance; Modeling and pricing of derivatives; Insurance and asset pricing; Portfolio management; Pricing; Revenue sharing and revenue management.
Planning, Scheduling and Control in Manufacturing Systems: Operations management; Project management; Quality management; Hierarchical production planning; Facilities planning; Reconfigurable and flexible systems; Enterprise resource planning; Product variety management; Dynamic, reactive and proactive scheduling.
Data Science and Machine Learning: Supervised, semisupervised and unsupervised machine learning models, Optimization for emerging machine learning problems, developing new and efficient machine learning models and algorithms for novel applications, transfer and continuous learning, statistical learning theory, online learning, reinforcement learning, multiarmed bandit problems, longitudinal data analysis, timeseries modeling and analytics.
Deep Learning: Learning theoretical aspects of deep learning models, new deep learning tools for modern applications involving text, image, audio and video data, deep learning for industrial and business applications, deep learning for OR.