Stephen H. Muggleton (born 6 December 1959, son of Louis Muggleton) is a Professor of Machine Learning and Head of the Computational Bioinformatics Laboratory at Imperial College London.[1,3,4,5] He completed his doctorate in 1987 at the University of Edinburgh under Donald Michie with the thesis "Inductive acquisition of expert knowledge" (https://www.era.lib.ed.ac.uk/handle/1842/8124).[2]
His research focuses on inductive logic programming, automation of science, relational learning, computational logic, and logic programming, and he is particularly noted for his work on inductive logic programming and the Robot Scientist.[1] His institutional affiliations include Imperial College London, the University of Oxford, the Turing Institute, the University of York, and Nanjing University. He holds the honors FREng, FBCS, FIET, and FAAAI, and maintains a website at http://www.doc.ic.ac.uk/~shm.[3]
Education
Muggleton received his Bachelor of Science degree in computer science in 1982 and his Doctor of Philosophy in artificial intelligence in 1986, supervised by Donald Michie at the University of Edinburgh.[6]
Career
Following his PhD, Muggleton worked as a postdoctoral research associate at the Turing Institute in Glasgow (1987–1991) and later as an EPSRC Advanced Research Fellow at Oxford University Computing Laboratory (OUCL) (1992–1997), where he founded the Machine Learning Group.[7] In 1997 he moved to the University of York and in 2001 to Imperial College London.[1,3,4,5] From 2025, Muggleton has joined Nanjing University as a full-time professor.
Research
Muggleton's research interests are primarily in Artificial intelligence.[8] From 1997 to 2001 he held the Chair of Machine Learning at the University of York and from 2001 to 2006 the EPSRC Chair of Computational Bioinformatics at Imperial College in London.[1,3,4,5] Since 2013 he holds the Syngenta/Royal Academy of Engineering Research Chair as well as the post of Director of Modelling for the Imperial College Centre for Integrated Systems Biology.[9]
He is known for founding the field of Inductive logic programming.[10,11,12,13,14] In this field he has made contributions to theory introducing predicate invention, inverse entailment and stochastic logic programs. He has also played a role in systems development where he was instrumental in the systems Duce, Cigol, Golem, Progol and Metagol and applications – especially biological prediction tasks.[7] He worked on a Robot Scientist together with Ross D. King that is capable of combining Inductive Logic Programming with active learning.[17,16] His present work concentrates on the development of Meta-Interpretive Learning, a new form of Inductive Logic Programming which supports predicate invention and learning of recursive programs.
