Prof. Michael G. Madden is the Established Professor of Computer Science in University of Galway, and leads the Machine Learning Research Group that he set up in 2001. His research focuses on new theoretical advances in machine learning, motivated by addressing important data-driven applications in medicine, engineering, and the physical sciences.

His machine learning research encompasses a variety of areas such as dynamic Bayesian networks for medical knowledge representation, deep learning techniques, and reinforcement learning for robotics. He has also gained international experience as a Visiting Research Scientist at University of California Berkeley, University of California Irvine and University of Helsinki. He founded a university spin-out company, AnalyzeIQ Ltd, that develops machine learning software for chemical data analysis, based on his research.

Prof. Madden also lectures on machine learning and deep learning topics, actively contributing to the academic community and mentoring future researchers in the field. He is the chair of the ITAG Deep Learning forum, and engages strongly with industry on research and executive education. He has extensive experience of public communications including articles and interviews in print, on radio and television, on topics related to AI.

Michael's research work is focused on new theoretical advances in machine learning and deep learning, motivated by important practical applications, on the basis that challenging applications foster novel algorithms which in turn enable new applications. He is interested in combining data-driven learning with background knowledge.

Specific research topics include:

• Artificial intelligence
• Data-driven machine learning
• New methods for combining domain knowledge with data
• Algorithms for classification and numeric prediction
• Time series data analysis
• Probability, reasoning under uncertainty, and Bayesian networks
• Reinforcement learning
• Practical applications machine learning in science, engineering and medicine. 

• Algorithms and data structures
• Machine learning
• Deep learning
• Co-creating with AI tools