Mainly interested in innovative applications to deep learning, especially vision related ones and expanding unsupervised learning via research in Variational Autoencoders and Generative Adversarial networks. Also, very interested in the area of explorative reinforcement learning, which can provide general purpose models that can then be applied to more specific areas.
Theoretical notions behind computer science. In particular, automated reasoning and its relation to ontological management of data. Additionally, the mathematical and theoretical study of software modularity and other more general notions of modularity and elasticity.
Representation learning using deep neural networks. Representing sets, with applications to information retrieval, content-based recommendation and generative models. Adversarial learning for fair decision making.
Computational linguistics, natural language processing, cognitive modelling, complex networks. Currently focused on linguistic alignment & spreading phenomena in social media, & agent-based models of language change.