- Fundamental performance limits (and algorithms) for dictionary learning (e.g., matrix factorization), ranking, and deep learning architectures;
- Learning in the presence of privacy constraints;
- Learning in the large alphabet regime;
- Learning of graphical models and other statistical models.
- Multi-user information theory;
- Strong converse and second-order asymptotics;
- Error exponent analysis and the method of types;
- Information-theoretic security;
- Quantum information.