I gave a talk at the workshop on how the synthesis of logic and machine learning, especially areas such as statistical relational learning, can enable interpretability.

I gave a talk on decision-theoretic planning via probabilistic programming at Oxford. Slides are here.

I gave a talk and a tutorial at the Hybrid reasoning workshop at Aachen, Germany.

I discussed the applications of probabilistic programming for automated planning, and for the tutorial, I covered approaches to unify logic and probability.

Applications are invited for a PhD position in Artificial Intelligence, to be based in the School of Informatics at the University of Edinburgh. The position is an opportunity to combine cutting-edge research at the intersection of knowledge representation and machine learning, in service of enabling explainability in AI, and yield interpretable decision-making models.

More information can be found in the link of the post.

I’m thrilled to be a faculty fellow at the Alan Turing Institute.

I gave a seminar on extending the expressiveness of probabilistic relational models with first-order features, such as universal quantification over infinite domains.

We study planning in relational Markov decision processes involving discrete and continuous states and actions, and an unknown number of objects (via probabilistic programming).