Research Interests
My research interests are in the areas of Machine Learning, Data Science and Mining, and Artificial Intelligence in general. More specifically, my interests are a bit eclectic, but there are two major themes:
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Multi-label classification, multi-target and structured-output prediction (modelling multiple interconnected tasks or subtasks)
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Learning from sequential data and data streams, which includes sequential decision making, autonomous agents, and reinforcement learning
The two themes are often connected; since sequential implies multiple and often vice versa. With regard to both I am specifically interested in themes of explainability, uncertainty analysis, robustness and reliability, domain shift/concept drift, transfer learning and continual learning.
I’ll use any tools suited to the task, including deep neural network architectures and deep learning, probabilistic graphical models, Monte Carlo methods and MCMC and other methods from computational statistics, and classical machine learning algorithms such as decision trees and random forests.
I also enjoy tackling real-world problems (some listed below), and data science applications; including sensor networks and sensory data, transport and energy systems and medicine, biology and the natural sciences.
