Stephen Muggleton

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Stephen Muggleton
File:NewFellowPhoto.jpg
Stephen Muggleton 2010
Born (1959-12-06) 6 December 1959 (age 62)
Fields
Institutions
Alma mater University of Edinburgh
Thesis Inductive acquisition of expert knowledge (1987)
Doctoral advisor Donald Michie[2]
Doctoral students
Known for
Notable awards
Website
www.doc.ic.ac.uk/~shm

Stephen H. Muggleton FBCS, FIET, FAAAI,[6]FECCAI, FSB, FREng[7] (born 6 December 1959, son of Louis Muggleton) is Head of the Computational Bioinformatics Laboratory at Imperial College London.[1][8][9][10][11][12][13]

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.[14]

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.[15] In 1997 he took a post at the University of York and in 2001, he moved from there to Imperial College London.

Research

Muggleton's research interests[9][16] are primarily in Artificial intelligence. From 1997–2001 he held the Chair of Machine Learning at the University of York[17] and from 2001–2006 the EPSRC Chair of Computational Bioinformatics at Imperial College in London. Since 2013 he holds the Syngenta/Royal Academy of Engineering Research Chair[18] Chair as well as the post of Director of Modelling for the Imperial College Centre for Integrated Systems Biology.[18] He is known for founding the field of Inductive logic programming.[19][20][21][22] 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, Golem and Progol[23][24] and applications — especially biological prediction tasks.

He worked on a Robot Scientist together with Stephen Emmott[25] that would be capable of combining inductive logic with probabilistic reasoning.[26] His present work concentrates on the development of Meta-Interpretive Learning,[27] a new form of Inductive Logic Programming which supports predicate invention and learning of recursive programs.

References

  1. 1.0 1.1 Stephen Muggleton's publications indexed by Google Scholar, a service provided by Google
  2. 2.0 2.1 Stephen Muggleton at the Mathematics Genealogy Project
  3. Moyle, Stephen Anthony (2003). An investigation into theory completion techniques in inductive logic programming (PhD thesis). University of Oxford.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
  4. Santos, Jose Carlos Almeida (2010). Efficient learning and evaluation of complex concepts in inductive logic programming (PhD thesis). Imperial College London.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
  5. http://www.raeng.org.uk/about/fellowship/fellowslist.htm List of Fellows of the Royal Academy of Engineering
  6. http://www.aaai.org/Awards/fellows-list.php
  7. http://www.raeng.org.uk/research/researcher/chairs/currentapp.htm Research Chairs: Current and Recently Completed at the Royal Academy of Engineering
  8. "Professor Stephen H. Muggleton". Academic staff list. Imperial College. Retrieved 8 August 2010.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
  9. 9.0 9.1 Stephen Muggleton's publications indexed by the DBLP Bibliography Server at the University of Trier
  10. Grants awarded to Stephen Muggleton by the Engineering and Physical Sciences Research Council
  11. Stephen Muggleton's publications indexed by the Scopus bibliographic database, a service provided by Elsevier.
  12. 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: 277. doi:10.1016/0004-3702(95)00122-0.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
  13. Stephen Muggleton from the Association for Computing Machinery (ACM) Digital Library
  14. Muggleton, Stephen (1987). Inductive acquisition of expert knowledge (PhD thesis). University of Edinburgh.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
  15. Muggleton, S. (1997). "Learning from positive data". 1314: 358–376. doi:10.1007/3-540-63494-0_65. Cite journal requires |journal= (help)<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
  16. List of publications from Microsoft Academic Search
  17. Muggleton, S. (1999). "Scientific knowledge discovery using inductive logic programming". Communications of the ACM. 42 (11): 42. doi:10.1145/319382.319390.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
  18. 18.0 18.1 "Prof Stephen Muggleton". The Royal Institution of Great Britain. Retrieved 8 August 2010.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
  19. 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.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
  20. Muggleton, S. (1991). "Inductive logic programming". New Generation Computing. 8 (4): 295–318. doi:10.1007/BF03037089.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
  21. Muggleton, S. (1995). "Inverse entailment and progol". New Generation Computing. 13 (3–4): 245–286. doi:10.1007/BF03037227.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
  22. 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. 1314. p. 23. doi:10.1007/3-540-63494-0_46. ISBN 978-3-540-63494-2.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
  23. "Golem". AI Japanese Institute for Science. Retrieved 8 August 2010.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
  24. Michalski, R.; Tecuci, G. (1994). Machine learning: a multistrategy approach (Book). Morgan Kaufmann. p. 780. ISBN 0-934613-09-5. Retrieved 8 August 2010.CS1 maint: multiple names: authors list (link)<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
  25. 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. doi:10.1038/nature02236. PMID 14724639.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
  26. "What computing can teach biology, and vice versa". The Economist. 12 July 2007. Retrieved 8 August 2010.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>(subscription required)
  27. Muggleton, S. H.; Lin, D.; Tamaddoni-Nezhad, A. (2015). "Meta-interpretive learning of higher-order dyadic datalog: Predicate invention revisited". Machine Learning. doi:10.1007/s10994-014-5471-y.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>