I am broadly interested in the design and analysis of machine learning algorithms for (weakly) supervised learning problems occurring in practice. Specific areas of interest include:
Weakly-supervised learning (e.g. learning from label noise, positive and unlabelled learning)
Classification with real-world constraints (e.g. class imbalance, fairness)
Matrix factorisation and applications (e.g. collaborative filtering, link prediction)
Temporal point processes and their inference
Relations amongst foundational problems (e.g. class-probability estimation, bipartite ranking)