1.1  Integer Programming
1.2  Mixed Integer Non-linear Programming
1.3  Approximation and Online Algorithms
1.4  Combinatorial Optimization and  Graph Theory
1.5  Machine Learning and Discrete Optimization
1.6.  Computational Aspects of Discrete Optimization

 

2.1  Continuous Optimization in Statistics, Signal Processing, and Machine Learning
2.2  Global Optimization
2.3  Algorithmic Foundations of Convex and Nonconvex Optimization
2.4  Conic and Polynomial Optimization
2.5  Variational analysis, Variational inequalities, and Games
2.6  Optimal control and PDE constrained optimization

 

Cluster 3.  Optimization under Uncertainty 

3.1  Stochastic optimization (Both Discrete and Continuous)
3.2  Robust and Distributionally Robust Optimization
3.3  Stochastic Dynamic Opt. and Reinforcement Learning
3.4  Stochastic and Adversarial Games (Both Discrete and Continuous)

 

Cluster 4.  Computation and Applications
4.1  Mathematics of data science
4.2  Networks, transportation and logistics
4.3  Healthcare, manufacturing and scheduling
4.4  Energy
4.5  Natural, life and social sciences
4.6  Implementation and software
4.7  High performance and quantum computing