Research Interests
My interests are in the areas of Machine Learning and Artificial Intelligence (AI).
In particular, I am interested in all kinds of temporal settings, including: learning from data streams; continual learning; transfer learning and domain adaptation (adapting to concept drift — or robustness against it); in sequential data and time series including particularly, reinforcement learning and multi-agent systems.
I focus on aspects such as uncertainty analysis, explainability, trustworthiness and reliability, particularly in the context of of probabilistic machine learning, including Bayesian inference and Monte Carlo methods.
In terms of tasks and applications I work on predictive maintenance, diagnostics and forecasting, and control systems (typically via reinforcement learning and sequence modelling). I have worked and published in domains involving sensor networks and sensory data, complex transport and energy systems, medicine, as well as biology/ecology and the natural sciences.
I used to hold a strong interest in multi-label classification, multi-target and structured-output prediction, and have followed the evolution of these areas into their modern contexts as deep learning, generative modelling, and sequential decision making.
Here are some high-level short presentations (pdf) of a subset of topics of interest to me (2022) and ./slides/of the ORAILIX team (2025) (see also the DaSciM team web page). As follows a selection of research activity, a lot of which involves the work of PhD students (and other colleagues).
