Stephen Muggleton

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Template:Short description Template:DMCA Template:DMCA Script error: No such module "Template wrapper".Script error: No such module "Check for conflicting parameters". Stephen H. Muggleton (born 6 December 1959, son of Louis Muggleton) is Professor of Machine Learning and Head of the Computational Bioinformatics Laboratory at Imperial College London.[1][2][3][4][5][6][7]

Education

Muggleton received his Bachelor of Science degree in computer science (1982) and Doctor of Philosophy in artificial intelligence (1986) supervised by Donald Michie at the University of Edinburgh.[8]

Career

Following his PhD, Muggleton went on to work as a postdoctoral research associate at the Turing Institute in Glasgow (1987–1991) and later an EPSRC Advanced Research Fellow at Oxford University Computing Laboratory (OUCL) (1992–1997) where he founded the Machine Learning Group.[9] In 1997 he moved to the University of York and in 2001 to Imperial College London. From 2025, Muggleton has joined Nanjing University as a full-time professor.

Research

Muggleton's research interests[3] are primarily in Artificial intelligence. From 1997 to 2001 he held the Chair of Machine Learning at the University of York[10] and from 2001 to 2006 the EPSRC Chair of Computational Bioinformatics at Imperial College in London. Since 2013 he holds the Syngenta/Royal Academy of Engineering Research Chair[11] as well as the post of Director of Modelling for the Imperial College Centre for Integrated Systems Biology.[11] He is known for founding the field of Inductive logic programming.[12][13][14][15][16] 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,[17] Progol and Metagol[18] and applications – especially biological prediction tasks.

He worked on a Robot Scientist together with Ross D. King[19] that is capable of combining Inductive Logic Programming with active learning.[20] His present work concentrates on the development of Meta-Interpretive Learning,[18] a new form of Inductive Logic Programming which supports predicate invention and learning of recursive programs.

References

  1. ^ Stephen Muggleton publications indexed by Google ScholarTemplate:EditAtWikidata
  2. ^ Page Module:Citation/CS1/styles.css has no content."Professor Stephen H. Muggleton". Academic staff list. Imperial College. Retrieved 8 August 2010.
  3. ^ a b Stephen Muggleton at DBLP Bibliography ServerTemplate:EditAtWikidata
  4. ^ Grants awarded to Stephen Muggleton by the Engineering and Physical Sciences Research Council
  5. ^ Stephen Muggleton's publications indexed by the Scopus bibliographic database. (subscription required)
  6. ^ Page Module:Citation/CS1/styles.css has no content.Srinivasan, A.; Muggleton, S.H.; Sternberg, M.J.E.; King, R.D. (1996). "Theories for mutagenicity: A study in first-order and feature-based induction". Artificial Intelligence. 85 (1–2): 277–299. doi:10.1016/0004-3702(95)00122-0. hdl:10338.dmlcz/135595.
  7. ^ Template:ACMPortal
  8. ^ Page Module:Citation/CS1/styles.css has no content.Muggleton, Stephen (1987). Inductive acquisition of expert knowledge (PhD thesis). University of Edinburgh. hdl:1842/8124.
  9. ^ Page Module:Citation/CS1/styles.css has no content.Muggleton, S. (1997). "Learning from positive data". Inductive Logic Programming. Lecture Notes in Computer Science. Vol. 1314. pp. 358–376. doi:10.1007/3-540-63494-0_65. ISBN 978-3-540-63494-2. S2CID 18451163.
  10. ^ Page Module:Citation/CS1/styles.css has no content.Muggleton, S. (1999). "Scientific knowledge discovery using inductive logic programming". Communications of the ACM. 42 (11): 42–46. doi:10.1145/319382.319390. S2CID 1013641.
  11. ^ a b Page Module:Citation/CS1/styles.css has no content."Prof Stephen Muggleton". The Royal Institution of Great Britain. Archived from the original on 25 June 2010. Retrieved 8 August 2010.
  12. ^ Page Module:Citation/CS1/styles.css has no content.Muggleton, S. (1991). "Inductive logic programming". New Generation Computing. 8 (4): 295–318. doi:10.1007/BF03037089. S2CID 5462416.
  13. ^ Muggleton S.H. "Inductive Logic Programming", Academic Press, 1992.
  14. ^ Page Module:Citation/CS1/styles.css has no content.Muggleton, S. (1995). "Inverse entailment and progol". New Generation Computing. 13 (3–4): 245–286. CiteSeerX 10.1.1.31.1630. doi:10.1007/BF03037227. S2CID 12643399.
  15. ^ Page Module:Citation/CS1/styles.css has no content.Muggleton, S.; De Raedt, L. (1994). "Inductive Logic Programming: Theory and methods". The Journal of Logic Programming. 19–20: 629–679. doi:10.1016/0743-1066(94)90035-3.
  16. ^ Page Module:Citation/CS1/styles.css has no content.Muggleton, S.; Page, D.; Srinivasan, A. (1997). "An initial experiment into stereochemistry-based drug design using inductive logic programming". Inductive Logic Programming. Lecture Notes in Computer Science. Vol. 1314. p. 23. doi:10.1007/3-540-63494-0_46. ISBN 978-3-540-63494-2.
  17. ^ Page Module:Citation/CS1/styles.css has no content."Golem". AI Japanese Institute for Science. Retrieved 8 August 2010.
  18. ^ a b Page Module:Citation/CS1/styles.css has no content.Muggleton, S. H.; Lin, D.; Tamaddoni-Nezhad, A. (2015). "Meta-interpretive learning of higher-order dyadic datalog: Predicate invention revisited". Machine Learning. 100: 49–73. doi:10.1007/s10994-014-5471-y. hdl:10044/1/23814.
  19. ^ Page Module:Citation/CS1/styles.css has no content.King, R. D.; Whelan, K. E.; Jones, F. M.; Reiser, P. G. K.; Bryant, C. H.; Muggleton, S. H.; Kell, D. B.; Oliver, S. G. (2004). "Functional genomic hypothesis generation and experimentation by a robot scientist". Nature. 427 (6971): 247–252. Bibcode:2004Natur.427..247K. doi:10.1038/nature02236. PMID 14724639. S2CID 4428725.
  20. ^ Page Module:Citation/CS1/styles.css has no content."What computing can teach biology, and vice versa". The Economist. 12 July 2007. Retrieved 8 August 2010.(subscription required)

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