James Cussens

[Research] [PhD supervision] [Projects] [Software] [Teaching] [Professional Activities] [Administration]
[Personal history] [Contact information] [Dept home page]

Research

Google scholar profile

GOBNILP software for exact Bayesian network learning

Recent papers

• Mark Barlett and James Cussens. Advances in Bayesian Network Learning using Integer Programming. Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI 2013). To appear
• Joanne Powell, Matthew J. Collins, James Cussens, Norman MacLeod and Kirsty E.H. Penkman. Results from an amino acid racemization inter-laboratory proficiency study; design and performance evaluation. Quaternary Geochronology. In Press.
• James Cussens, Mark Bartlett, Elinor M. Jones and Nuala A. Sheehan. Maximum Likelihood Pedigree Reconstruction using Integer Linear Programming. Genetic Epidemiology, 37(1):69-83, Janary 2013.
• James Cussens. Leibniz on Probability and Statistics. In Maria Rosa Antognazza, editor, The Oxford Handbook of Leibniz. Oxford University Press. (Forthcoming).
• James Cussens. Column generation for exact BN learning: Work in progress. Proc. ECAI-2012 workshop on COmbining COnstraint solving with MIning and LEarning (CoCoMile 2012).
• James Cussens. An upper bound for BDeu local scores. Proc. ECAI-2012 workshop on algorithmic issues for inference in graphical models (AIGM 2012).
• James Cussens. Online Bayesian inference for the parameters of PRISM programs. Machine Learning. 89(3), 279-297, 2012 (Preprint available) (DOI: 10.1007/s10994-012-5305-8)
• James Cussens. Bayesian network learning with cutting planes (PDF). In Fabio G. Cozman and Avi Pfeffer, editors, Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), pages 153-160, Barcelona, 2011. AUAI Press.

Full catalogue of papers

Recent talks

• Integer Programming for Bayesian Network Structure Learning , Helsinki Institute for Information Technology, 4 April 2013.
• Integrating Constraint Programming and Integer Programming with SCIP, KU Leuven, 25 February 2013.
• Bayesian network learning with SCIP: model selection and model averaging, SCIP workshop, Darmstadt, 8 October 2012.
• Model averaging for graphical models by repeated optimal model search, SuSTain workshop on Structure and uncertainty, Bristol, 26 September 2012.
• Bayesian network structure learning using integer linear programming: results and prospects, COSA Workshop 2012, Zentrum Mathematik, TU Munich, 6 September 2012.

Artificial Intelligence at York

PhD supervision

Current PhD students

• Garo Panikian - Statistical inference of dynamical systems with application to modelling fish populations
• Eman Aljohani - Informative priors for learning graphical models

Former students

• Waleed Alsanie - Learning PRISM programs
• Joanne Powell - PrediCtoR: Predicting the Recovery of Ancient DNA and Ancient Proteins (with Matthew Collins, Archaeology)
• Adel Aloraini - Extending the graphical represetation of KEGG pathways for a better understanding of prostate cancer using machine learning
• Barnaby Fisher - Inductive Logic Programming and Mercury (MSc by Research)
• Heather Maclaren - Inductive Logic Programming for Software Agents: Algorithms and Implementations

Topics for prospective PhD students

My main research interests are in machine learning, particularly focusing on graphical models and inductive logic programming and combinations thereof - such a combination is known as statistical relational learning or probabilistic inductive logic programming. I also continue to have an interest in the application of machine learning techniques to natural language.

To undertake research in these areas it is necessary to have a strong mathematical background. Unfortunately, I have no specific funding available to support graduate students. Our department does award funded research studentships on a competitive basis.

Software

• GOBNILP software for exact Bayesian network learning
• gPy is a collection of Python modules for manipulating discrete hierarchical models (including Bayesian nets). It is used to support the teaching of Algorithms for Graphical Models.
• The MCMCMS (Markov chain Monte Carlo over Model Structures) system uses Stochastic Logic Programs (SLPs) to define priors for Bayesian inference. The code was written by Nicos Angelopoulos. The user guide provides a tutorial on its use.
• Pepl is an implementation of the Failure-Adjusted Maximisation (FAM) algorithm.This is an instance of the EM algorithm which produces maximum likelihood estimates for the parameters of SLPs. The code was written by Nicos Angelopoulos.
• Aaron Bate, a final year student in this department, has produced software for animating the construction of Prolog proof trees (the software draws a graphical representation of the proof tree). It uses Sicstus Prolog and Tcl/Tk. You can download the software as a gzipped tar file. I have included a simple Prolog SLP interpreter which allows you to sample from a distribution over proof trees and where each proof tree determines an acyclic digraph (the structural element of a Bayesian net). See the file slp_readme in the distribution for an explanation.

Projects

• Imprecision and importance: probabilistic graphical models in toxicology 01/07/13-01/12/15. Funded by NC3RS.
• A graphical model approach to pedigree construction using constrained optimisation 03/10/11-03/10/14. Funded by the MRC.
• Stochastic Logic Programs for MCMC (with Nicos Angelopoulos) 01/09/03--31/08/05. Funded by EPSRC's MathFIT programme. EPSRC's page for this project.
• APRIL II: Applications of Probabilistic Inductive Logic Programming 02/01/04--02/01/07. Funded by the EU IST programme.

Teaching

Currently, at York:

• Can Machines Think? (CANM)
• Machine Learning and Applications (MLAP)
• Introduction to Artificial Intelligence (ARIN)
• Introduction to Machine Learning (IML)

Professional Activities

Programme chair

• ILP00 (with Alan Frisch)
• LLL99 LLL05 (with Claire Nédellec)

Editorial duties

• Machine Learning Action Editor

Member

• ENBIS-SFdS 2014 Spring Meeting on Graphical causality models: Trees, Bayesian Networks and Big Data Scientific Committee
• SIGNLL International Advisory Committee

Invited speaker

• ILP-MLG-SRL 09
• UKKDD-2007
• AC05
• ILP04

Area chair/Senior PC

• IJCAI-13
• ECAI-12
• IJCAI-11, AAAI-11
• ECML/PKDD-08
• ECML/PKDD-07
• ECML/PKDD-06
• ICML05

Co-organiser

• IJCAI 2013 Tutorial on Optimal Algorithms for Learning Bayesian Networks with Brandon Malone and Changhe Yuan
• ICML 2012 Workshop on Statistical Relational Learning (SRL-12) together with Kristian Kersting, Sriraam Natarajan, and David Poole.

PC member

• EMNLP 2013, UAI 2013, NAACL-HLT 2013, ILP 2013, ECML/PKDD 2013 ICML 2013 LML workshop at ECML/PKDD 2013
• StaRAI-12, UAI 2012, CoCoMile 2012, ACL 2012, Cognitive 2012, ICML 2012, ILP 2012, AAAI-12, ECML/PKDD-12, KR 2012
• ECML/PKDD-11, UAI 2011, ILP 2011, ICML 2011
• AAAI-10, ILP 2010, ECAI-2010, ECML/PKDD 2010, SBIA 2010
• Terminologie et intelligence artificielle (TIA - 2009), ILP-09, SRL-09, IJCAI-09, ICML 09, AISTATS 09, CoNLL 09, NAACL-HLT 09, EACL Cognitive 2009, NAACL-2009 Workshop on Unsupervised and Minimally Supervised Learning of Lexical Semantics
• CoNLL 08, ILP 08, ECAI 08, SBIA 08, ICML 08
• ICML 07, UAI07, ILP07, ACL-2007 Workshop on Cognitive Aspects of Computational Language Acquisition, TIA'07
• AAAI-06, UAI06, ILP06, SRL06, CoNLL06
• UAI05, ICML05, ECML/PKDD05, ILP05, LLLL, CoNLL05, TIA05
• ECML04 , CIFT04, UAI04 , ICML04 , SRL04 , CoNLL04 , Psycho-computational models ...
• UAI03, ICML03 , ILP03, CoNLL03 , Acquisition, apprentissage et ... , SRL2003 , ECML03
• UAI02, CIFT02, ICML02 , ECML02 , ILP02, CoNLL02
• ECML01 , CoNLL01 , ILP01 , LLL01
• CoNLL00 , ILP00 , LLL00
• ILP99 , LLL99
• ILP98

Reviewer

• NIPS13
• NIPS12
• EACL09, NIPS09
• NIPS08
• EACL06
• IJCAI05
• ACL04, NIPS04

Miscellaneous

• Attendee at the 2011 Dagstuhl seminar on Constraint Programming meets Machine Learning and Data Mining
• Attendee at the 2007 Dagstuhl seminar on Probabilistic, Logical and Relational Learning - A Further Synthesis
• Attendee at the 2005 Dagstuhl seminar on Probabilistic, Logical and Relational Learning - Towards a Synthesis

Administration

• Chair of the MRes Computational Biology Executive Committee
• Student Projects Organiser

Personal history

Contact information