I am interested in the intersection of learning and model-based planning from a theoretical viewpoint.

Planning is focused on long horizon reasoning and complex problem solving with guarantees. However, planning models suffer from computational complexity. The tradeoff with planning is between expressivity and scalability: we want planning models which are expressive enough to model practical, real life problems, but are also solvable with reasonable computational resources to be actually useful.

Learning is focused on pattern matching and data compression. However, learning models generally do not have guarantees with their outputs. The tradeoff with learning is between computation, expressivity and generalisability: we want learning models which are expressive enough to capture certain patterns, but are also tractable and able to generalise to unseen outputs.

The two main problems I am currently interested in currently are 
1. looking into problem methodologies that make model-based planning more applicable and feasible
2. discovering and/or developing theory for the field of learning and planning to improve generalisation, scalability, and solution quality