Dr. Cory J. Butz
Department of Computer Science
Faculty of Science
University of Regina

Presenting our AAAI 2019 paper, entitled Deep Convolutional Sum-Product Networks, with my graduate students J. Oliveira, A. dos Santos, and A.L. Teixeira.

In 2011, he gave invited research seminars at Cambridge and MIT based in part on this work:

The Semantics of Intermediate CPTs in Variable Elimination.

In 2007, he was invited by a Google talent scout to visit Google's Mountain View headquarters in order to discuss an earlier version of the following paper:

A Join Tree Probability Propagation Architecture for Semantic Modeling.

His research primarily focuses on Bayesian networks. Bayesian networks use probability theory as a formal framework for uncertainty management in artificial intelligence.

He received the 2014 University of Regina Alumni Association Award for Excellence in Teaching.

He is a past president of the Canadian Artificial Intelligence Association.

Valid CSS!

Dr. Butz's Schedule
Winter 2025
Time Monday Tuesday Wednesday Thursday Friday
 
9:30-10:30
 
 Research Office Hour Research Office Hour Research
 
10:30-11:20
 
 CS110 Office Hour CS110 Office Hour CS110
 
11:30-12:20
 
 Lunch Lunch Lunch Lunch Lunch
 
12:30-4:20
 
 Research Research Research Research Research
CS110 Basic Information
Assignment 0. Zero marks.
  • Question 1. Play the light-bot video game.
  • Complete level 6: at the very minimum.
  • Complete level 7: shows you how to reuse functions.
  • Complete level 8: Good job! Now you're thinking like a programmer.
  • Question 2.Watch this video. I will have a 3D-printer demonstrated in class this semester.
Contact
Office: CW308.10
Telephone: (306) 585-4856
Email: cory DOT butz AT uregina DOT ca
Smail: Dr. Cory Butz
Department of Computer Science
University of Regina
3737 Wascana Parkway
Regina, Saskatchewan,
Canada S4S 0A2
Publications

Edited Proceedings

  1. C.J. Butz and P. Lingras (Eds.), Advances in Artificial Intelligence: 24th Canadian Conference on Artificial Intelligence, Canadian AI 2011, Lecture Notes in Artificial Intelligence 6657, Springer, Berlin, 2011.
  2. A. An, J. Stefanowski, S. Ramanna, C.J. Butz, W. Pedtycz and G. Wang (Eds.), Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, 11th International Conference, RSFDGrC 2007, Lecture Notes in Artificial Intelligence 4482, Springer, Berlin, 2007.

Invited Papers

  1. C.J. Butz, Introducing Darwinian Networks, Uncertain Reasoning track in the Twenty-eighth International Florida Artificial Intelligence Research Society Conference (FLAIRS), 604 - 609, 2015.
  2. C.J. Butz, Evaluating Probabilistic Inference Techniques: a Question of "When," not "Which," the 5th International Conference on Scalable Uncertainty Management (SUM'2011), 38 - 51, 2011.
  3. C.J. Butz, Current Trends in Bayesian Network Inference, the 2nd Indian International Conference on Artificial Intelligence (IICAI-07), 1186 - 1205, 2007.

Papers in Refereed Journals

  1. C.J. Butz, J.S. Oliveira, A. dos Santos, and A.L. Madsen, An Empirical Study of Bayesian Network Inference with Simple Propagation, International Journal of Approximate Reasoning, Vol. 92, 198-211, 2018.
  2. C.J. Butz, A. dos Santos, J.S. Oliveira, and C. Gonzales, An Empirical Study of Testing Independencies in Bayesian Networks using rp-Separation, International Journal of Approximate Reasoning, Vol. 92, 270-278, 2018.
  3. C.J. Butz, A. dos Santos, J.S. Oliveira, and C. Gonzales. On a Simple Method for Testing Independencies in Bayesian Networks, Computational Intelligence, Vol. 34, No. 3, 789-801, 2018.
  4. C.J. Butz, J.S. Oliveira and A. dos Santos, On Darwinian Networks, Computational Intelligence, Vol. 33, No. 4, 629-655, 2017.
  5. C.J. Butz, J.S. Oliveira, and A.L. Madsen, Bayesian Network Inference using Marginal Trees, International Journal of Approximate Reasoning, Vol. 68, 127-152, 2016.
  6. M. Kirzinger, C.J. Butz, and J. Stavrinides, Inheritance of Pantoea Type III Secretion Systems through both Vertical and Horizontal Transfer, Molecular Genetics and Genomics, Vol. 290, 2075-2088, 2015.
  7. H.D. Hadjistavropoulos, N.E. Pugh, M.N. Nugent, H. Hesser, G. Andersson, M. Ivanov, C.J. Butz, G. Marchildon, G.J. Asmundson, B. Klein and D.W. Austin, Therapist-Assisted Internet-Delivered Cognitive Behaviour Therapy for Depression and Anxiety: Evidence to Practice, Journal of Anxiety Disorders, Vol. 28, 884-893, 2014.
  8. A.L. Madsen and C.J. Butz, Ordering Arc-Reversal Operations when Eliminating Variables in Lazy AR Propagation, International Journal of Approximate Reasoning, Vol. 54, No. 8, 1182-1196, 2013.
  9. C.J. Butz, K. Konkel and P. Lingras, Join Tree Propagation Utilizing Both Arc Reversal and Variable Elimination, International Journal of Approximate Reasoning, Vol. 52, No. 7, 948-959, 2011.
  10. H.D. Hadjistavropoulos, M. Thompson, M. Ivanov, C. Drost, C.J. Butz, B. Klein and D.W. Austin, Considerations in the Development of a Therapist-Assisted Internet Cognitive Behavior Therapy Service, Professional Psychology: Research and Practice, Vol. 42, No. 6, 463-471, 2011.
  11. P. Lingras and C.J. Butz, Conservative and Aggressive Rough SVR Modeling, Theoretical Computer Science, Vol. 412, 5885-5901, 2011.
  12. C.J. Butz, S. Hua, K. Konkel and H. Yao, Join Tree Propagation with Prioritized Messages, Networks, Vol. 55, No. 4, 350-359, 2010.
  13. P. Lingras and C.J. Butz, Rough Support Vector Regression, European Journal of Operational Research, Vol. 206, No. 2, 445-455, 2010.
  14. C.J. Butz, J. Chen, K. Konkel and P. Lingras, A Formal Comparison of Variable Elimination and Arc Reversal in Bayesian Network Inference, Intelligent Decision Technologies, Vol. 3, No. 3, 173--180, 2009.
  15. C.J. Butz, S. Hua, J. Chen and H. Yao, A Simple Graphical Approach for Understanding Probabilistic Inference in Bayesian networks, Information Sciences, Vol. 179, 699-716, 2009.
  16. C.J. Butz, H. Yao and S. Hua, A Join Tree Probability Propagation Architecture for Semantic Modeling, Journal of Intelligent Information Systems, Vol. 33, No. 2, 145-178, 2009.
  17. P. Lingras and C.J. Butz, Rough Set based 1-v-1 and 1-v-r Approaches to Support Vector Machine Multi-classification, Information Sciences, Vol. 177, 3782-3798, 2007.
  18. C.J. Butz, W. Yan and B. Yang, An Efficient Algorithm for Inference in Rough Set Flow Graphs, Transactions on Rough Sets, Vol. 5, 102-122, 2006.
  19. C.J. Butz, S. Hua and R.B. Maguire, A Web-based Bayesian Intelligent Tutoring System for Computer Programming, Web Intelligence and Agent Systems: An International Journal, Vol. 4, No. 1, 77-97, 2006.
  20. S.K.M. Wong and C.J. Butz, Constructing the Dependency Structure of a Multi-Agent Probabilistic Network, IEEE Transactions on Knowledge and Data Engineering, Vol. 13, No. 3, 395-415, May 2001.
  21. S.K.M. Wong, C.J. Butz, and D. Wu, On the Implication Problem for Probabilistic Conditional Independency, IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, Vol. 30, No. 6, 785-805, November 2000.
  22. S.K.M. Wong and C.J. Butz, A Bayesian Approach to User Profiling in Information Retrieval, Technology Letters, Vol. 4, No. 1, 50-56, 2000.
  23. S.K.M. Wong, C.J. Butz and Y. Xiang, Automated Database Schema Design Using Mined Data Dependencies, Journal of the American Society for Information Science, Vol. 49, No. 5, 455-470, April 1998.

Refereed Book Chapters

  1. P. Lingras, P. Bhalchandra, C.J. Butz and S. Asharaf, Rough Support Vectors: Classification, Regression, Clustering, Rough Sets and Intelligent Systems, Intelligent Systems Reference Library (ISRL), 42, Springer-Verlag Berlin Heidelberg, pp. 491-515, 2013.
  2. P. Lingras, C.J. Butz and P. Bhalchandra, Financial Series Forecasting using Dual Rough Support Vector Regression, Rough Sets: Selected Methods and Applications of Rough Sets to Management and Engineering, Springer, pp. 115-127, 2011.
  3. C.J. Butz, W. Yan, P. Lingras and Y.Y. Yao, The CPT Structure of Variable Elimination in Discrete Bayesian Networks, Advances in Intelligent Information Systems, SCI 265, Z.W. Ras and L.S. Tsay (Eds.), Springer, 245-257, 2010.
  4. H. Yao, C.J. Butz and H.J. Hamilton, ``Causal Discovery,'' Data Mining and Knowledge Discovery Handbook, 2nd ed., O. Maimon and L. Rokach (Eds.), Springer, pp. 949-958, 2010.
  5. C.J. Butz, S. Hua and R.B. Maguire, ``Web-based Bayesian Intelligent Tutoring Systems,'' Evolution of WEB in an Artificial Intelligence Environment, SCI 130, R. Nayek and L.C. Jain (Eds.), Springer-Verlag, 223-244, 2008.
  6. P. Lingras, S. Asharaf and C.J. Butz, ``Rough Clustering,'' Handbook of Granular Computing, W. Pedrycz, A. Skowron and V. Kreinovich (Eds.), Wiley, 969-985, 2008.
  7. C.J. Butz and W. Yan, ``Current Trends in Rough Set Flow Graphs,'' Rough Computing: Theories, Technologies and Applications, A.E. Hassanien, Z. Suraj, D. Slezak and P. Lingras (Eds.), Information Science Reference, 152-161, 2007.
  8. H. Yao, C.J. Butz and H.J. Hamilton, ``Causal Discovery,'' Data Mining and Knowledge Discovery Handbook, O. Maimon and L. Rokach (Eds.), Springer, pp. 945-955, 2005.
  9. C.J. Butz and M.J. Sanscartier, ``Towards Web Search Using Contextual Probabilistic Independencies'', Computational Web Intelligence: Intelligent Technology for Web Applications, Y. Zhang, A. Kandel, T.Y. Lin and Y.Y. Yao (Eds.), World Scientific, 149-166, 2004.
  10. S.K.M. Wong and C.J. Butz, ``The Membership Problem for Probabilistic and Data Dependencies'', in Technologies for Constructing Intelligent Systems, B. Bouchon-Meunier, L. Magdalena, and R.R. Yager (Eds.), Springer Verlag, Vol. 2, 73-84, 2002.
  11. S.K.M. Wong, Y.Y. Yao and C.J. Butz, ``Granular Information Retrieval'', Soft Computing in Information Retrieval: Techniques and Applications. F. Crestani and G. Pasi (Eds.), Springer Verlag, 317-331, 2000.

Refereed Conference Papers

  1. C.J. Butz, A.L. Madsen, J.S. Oliveira, Fast Arc Reversal, Twelfth International Conference on Probabilistic Graphical Models (PGM), to appear, 2024.
  2. J. Wang and C.J. Butz, Optimizing Rough Set Flow Graph Inference, International Joint Conference on Rough Sets (IJCRS), LNAI 14839, 329-342, 2024.
  3. C.J. Butz, J.S. Oliveira, A. dos Santos, A. Norris, K. Meredith, Upward Pass Semantics in Arithmetic Circuit Inference, Thirty-sixth Canadian Conference on Artificial Intelligence (AI), 2023.
  4. A.L. Madsen and C.J. Butz, A Comparison of Different Marginalization Operations in Simple Propagation, Seventeenth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU), 2023.
  5. C.J. Butz, J.S. Oliveira, R. Peharz, Sum-Product Network Decompilation, Tenth International Conference on Probabilistic Graphical Models (PGM), 2020.
  6. C.J. Butz, A. dos Santos, J.S. Oliveira, and A.L. Madsen, Exploiting Symmetry of Independence in d-Separation, Thirty-second Canadian Conference on Artificial Intelligence (AI), 42--54, 2019.
  7. A.L. Madsen, C.J. Butz, J.S. Oliveira, and A. dos Santos, Solving Influence Diagrams with Simple Propagation, Thirty-second Canadian Conference on Artificial Intelligence (AI), 68--79, 2019.
  8. C.J. Butz, A.L. Teixeira, J.S. Oliveira, and A. dos Santos, On the Tree Structure of Deep Convolutional Sum-Product Networks, Thirty-Second International Florida Artificial Intelligence Research Society Conference (FLAIRS), 500--503, 2019.
  9. C.J. Butz, J.S. Oliveira, A. dos Santos, A.L. Teixeira, Deep Convolutional Sum-Product Networks, Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), 3248--3255, 2019.
  10. J.S. Oliveira, C.J. Butz, and S. Zilles, A Two-Phase Method for Focused Learning in Sum-Product Networks, Third Workshop on Tractable Probabilistic Modeling, 2019.
  11. C.J. Butz, J.S. Oliveira, A. dos Santos, A.L. Teixeira, P. Poupart, A. Kalra, An Empirical Study of Methods for SPN Learning and Inference, Ninth International Conference on Probabilistic Graphical Models (PGM), 49--60, 2018.
  12. A.L. Madsen, C.J. Butz, J.S. Oliveira, A. dos Santos, Simple Propagation with Arc-Reversal in Bayesian Networks, Ninth International Conference on Probabilistic Graphical Models (PGM), 260--271, 2018.
  13. C.J. Butz, A. dos Santos, J.S. Oliveira, and J. Stavrinides, Efficient Examination of Soil Bacteria using Probabilistic Graphical Models, Thirty-first International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems, 315--326, 2018.
  14. C.J. Butz, J.S. Oliveira, and A.E. dos Santos, On Learning the Structure of Sum-Product Networks, IEEE Symposium Series on Computational Intelligence, 2997--3004, 2017.
  15. A. dos Santos, C.J. Butz, and J.S. Oliveira, On Converting Sum-Product Networks into Bayesian Networks, Thirtieth Canadian Conference on Artificial Intelligence (AI), 329--334, 2017.
  16. J.S. Oliveira, C.J. Butz, and A. dos Santos, Resolving Inconsistencies of Scope Interpretations in Sum-Product Networks, Thirtieth Canadian Conference on Artificial Intelligence (AI), 303--315, 2017.
  17. C.J. Butz, A. dos Santos, and J.S. Oliveira, On Finding Relevant Variables in Discrete Bayesian Network Inference, Thirtieth International Florida Artificial Intelligence Research Society Conference (FLAIRS), 730--735, 2017.
  18. C.J. Butz, A.E. dos Santos, J.S. Oliveira, Relevant Path Separation: A Faster Method for Testing Independencies in Bayesian Networks, Eighth International Conference on Probabilistic Graphical Models (PGM), 74 -- 85, 2016.
  19. C.J. Butz, J.S. Oliveira, A.E. dos Santos, and A.L. Madsen, On Bayesian Network Inference with Simple Propagation, Eighth International Conference on Probabilistic Graphical Models (PGM), 62 -- 73, 2016.
  20. C.J. Butz, A. dos Santos, J.S. Oliveira, and C. Gonzales, A Simple Method for Testing Independencies in Bayesian Networks, Twenty-ninth Canadian Conference on Artificial Intelligence (AI), 213--223, 2016.
  21. A.L. Madsen, C.J. Butz, J.S. Oliveira, A. dos Santos, On Tree Structures used by Simple Propagation for Bayesian Networks Inference, Twenty-ninth Canadian Conference on Artificial Intelligence (AI), 207--212, 2016.
  22. C.J. Butz, J.S. Oliveira, A. dos Santos, and A.L. Madsen, Bayesian Network Inference with Simple Propagation, Twenty-ninth International Florida Artificial Intelligence Research Society Conference (FLAIRS), 650 -- 655, 2016.
  23. C.J. Butz, A. dos Santos, J.S. Oliveira, and C. Gonzales, Testing Independencies in Bayesian Networks with i-Separation, Twenty-ninth International Florida Artificial Intelligence Research Society Conference (FLAIRS), 644 -- 649, 2016.
  24. C.J. Butz, J.S. Oliveira and A. dos Santos, Darwinian Networks, Twenty-eighth Canadian Conference on Artificial Intelligence (AI), 16--29, 2015.
  25. A.L. Madsen and C.J. Butz, Exploiting Semantics in Bayesian Network Inference Using Lazy Propagation, Twenty-eighth Canadian Conference on Artificial Intelligence (AI), 3--15, 2015.
  26. C.J. Butz, J.S. Oliveira and A. dos Santos, Determining Good Elimination Orderings with Darwinian Networks, Twenty-eighth International Florida Artificial Intelligence Research Society Conference (FLAIRS), 600 -- 603, 2015.
  27. C.J. Butz, J.S. Oliveira and A.L. Madsen, Bayesian Network Inference Using Marginal Trees, Seventh European Workshop on Probabilistic Graphical Models (PGM), 81--96, 2014.
  28. C.J. Butz, W. Yan and A.L. Madsen, On Semantics of Inference in Bayesian Networks, Twelfth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU), 73--84, 2013.
  29. A.L. Madsen and C.J. Butz, On the Tree Structure used by Lazy Propagation for Inference in Bayesian Networks, Twelfth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU), 400--411, 2013.
  30. C.J. Butz, W. Yan and A.L. Madsen, d-Separation: Strong Completeness of Semantics in Bayesian Network Inference, Twenty-sixth Canadian Conference on Artificial Intelligence (AI), 13--24, 2013.
  31. A.L. Madsen and C.J. Butz, On the Importance of Elimination Heuristics in Lazy Propagation, Sixth European Workshop on Probabilistic Graphical Models (PGM), 227--234, 2012.
  32. C.J. Butz, A.L. Madsen and K. Williams, Using Four Cost Measures to Determine Arc Reversal Orderings, Eleventh European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU), 110--121, 2011.
  33. C.J. Butz and W. Yan, The Semantics of Intermediate CPTs in Variable Elimination, Fifth European Workshop on Probabilistic Graphical Models (PGM), 41-49, 2010.
  34. C.J. Butz, K. Konkel and P. Lingras, Join Tree Propagation utilizing both Arc Reversal and Variable Elimination, Twenty-second International Florida Artificial Intelligence Research Society Conference (FLAIRS), 523--528, 2009.
  35. C.J. Butz, J. Chen, K. Konkel and P. Lingras, A Comparative Study of Variable Elimination and Arc Reversal in Bayesian Network Inference, Twenty-second International Florida Artificial Intelligence Research Society Conference (FLAIRS), 571--572, 2009.
  36. C.J. Butz, W. Yan, P. Lingras, K. Konkel and Y. Yao, On Variable Elimination in Discrete Bayesian Network Inference, 9th World Meeting of the International Society for Bayesian Analysis (ISBA08), 96--97, 2008.
  37. C.J. Butz, P. Lingras and K. Konkel, A Web-based Interface for Hiding Bayesian Network Inference, 17th International Symposium on Methodologies for Intelligent Systems (ISMIS08), 612--617, 2008.
  38. P. Lingras and C.J. Butz, Precision and Recall in Rough Support Vector Machines, the IEEE International Conference on Granular Computing, 654--658, 2007.
  39. C.J. Butz and S. Hua, An Improved LAZY-AR Approach to Bayesian network Inference, Nineteenth Canadian Conference on Artificial Intelligence (AI), 183--194, 2006.
  40. C.J. Butz and F. Fang, Sophisticated Indexes for Implementing Probabilistic Expert Systems, the 2nd Indian International Conference on Artificial Intelligence (IICAI-05), 609--620, 2005.
  41. C.J. Butz and P. Lingras, On the Practical Irrelevance of Diverging Implication between Probabilistic Conditional Independence and Embedded Multivalued Dependency, the 2nd Indian International Conference on Artificial Intelligence (IICAI-05), 2464--2475, 2005.
  42. C.J. Butz and F. Fang, Modelling Multiagent Bayesian networks with Inclusion Dependencies, the IEEE/WIC/ACM Conference on Intelligent Agent Technology (IAT05), 455--458, 2005.
  43. P. Lingras and C.J. Butz, Interval Set Representations of 1-v-r Support Vector Machine Multi-classifiers, the IEEE International Conference on Granular Computing, 193--198, 2005.
  44. C.J. Butz, W. Yan and B. Yang, The Computational Complexity of Inference using Rough Set Flow Graphs, 10th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC05), vol. 1, 335--344, 2005.
  45. D. Wu and C.J. Butz, On the Computational Complexity of Probabilistic Inference in Singly Connected Bayesian networks, 10th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC05), vol. 1, 581--590, 2005.
  46. P. Lingras and C.J. Butz, Reducing the Storage Requirements of 1-v-1 Support Vector Machine Multi-classifiers, 10th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC05), vol. 2, 166--173, 2005.
  47. C.J. Butz and F. Fang, Incorporating Evidence in Bayesian networks with the Select Operator, Eighteenth Canadian Conference on Artificial Intelligence (AI), 297--301, 2005.
  48. D.H. Hepting and C.J. Butz, An Integrated Approach to Discovery in Complex Information Spaces, Second International Workshop on Web-based Support Systems, 67--74, 2004.
  49. C.J. Butz, S. Hua and R.B. Maguire, A Web-based Intelligent Tutoring System for Computer Programming, the IEEE/WIC/ACM Conference on Web Intelligence (WI04), 159--165, 2004.
  50. Y. Yao, J. Yao, C.J. Butz, P. Lingras and D. Jutla, Web-based Support Systems: a Report of the WIC Canada Research Centre, the IEEE/WIC/ACM Conference on Web Intelligence (WI04), 787--788, 2004.
  51. H. Yao, H. Hamilton and C.J. Butz, A Foundational Approach for Mining Itemset Utilities from Databases, 2004 SIAM International Conference on Data Mining (SIAMDM04), 482--486, 2004.
  52. C.J. Butz and J. Liu, On the Implication Problem in Granular Knowledge Systems, the 2004 conference of the North American Fuzzy Information Processing Society (NAFIPS04), 63-68, 2004.
  53. C.J. Butz and P. Lingras, Granular Jointree Probability Propagation, the 2004 conference of the North American Fuzzy Information Processing Society (NAFIPS04), 69-72, 2004.
  54. P. Lingras and C.J. Butz, Interval Set Classifiers using Support Vector Machines, the 2004 conference of the North American Fuzzy Information Processing Society (NAFIPS04), 707-710, 2004.
  55. C.J. Butz, H. Yao and H. Hamilton, Towards Jointree Propagation with Conditional Probability Distributions, the 4th International Conference on Rough Sets and Current Trends in Computing (RSCTC04), 368-377, 2004.
  56. C.J. Butz, S. Hua and R.B. Maguire, Bits: a Bayesian Intelligent Tutoring System for Computer Programming, the 9th Western Canadian Conference on Computing Education (WCCCE04), 179-186, 2004.
  57. C.J. Butz and H. Geng, Comparing Hierarchical Markov Networks and Multiply Sectioned Bayesian Networks, 14th International Symposium on Methodologies for Intelligent Systems (ISMIS03), 544--553, 2003.
  58. C.J. Butz and J. Liu, A Query Processing Algorithm for Hierarchical Markov Networks, 2nd Annual Asia-Pacific Conference on Web Intelligence (WI03), 588--592, 2003.
  59. C.J. Butz, A General Coarsening Method for Granular Probabilistic Networks, 7th International Conference on Computer Science and Informatics (CSI03), 462--465, 2003.
  60. C.J. Butz, Constructing the Maximal Prime Decomposition of Bayesian Networks, 7th International Conference on Computer Science and Informatics (CSI03), 458--461, 2003.
  61. C.J. Butz, S.K.M. Wong and D. Wu, A New Inference Axiom for Probabilistic Conditional Independence, Sixteenth Canadian Conference on Artificial Intelligence (AI), 568--574, 2003.
  62. S.K.M. Wong, D. Wu and C.J. Butz, Probabilistic Reasoning in Bayesian Networks: a Relational Database Approach, Sixteenth Canadian Conference on Artificial Intelligence (AI), 583--590, 2003.
  63. C.J. Butz, Q. Hu and X.D. Yang, Critical Remarks on the Maximal Prime Decomposition of Bayesian Networks, 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC03), 682--685, 2003.
  64. C.J. Butz, H. Yao and H. Hamilton, A Non-Local Coarsening Result in Granular Probabilistic Networks, 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC03), 686-689, 2003.
  65. H. Yao, H. Hamilton and C.J. Butz, FD_Mine: Discovering Functional Dependencies in a Database Using Equivalences, 2002 IEEE International Conference on Data Mining (ICDM02), 729--732, 2002.
  66. S.K.M. Wong, D. Wu and C.J. Butz, Triangulation of Bayesian networks: a Relational Database Perspective, 3rd International Conference on Rough Sets and Current Trends in Computing (RSCTC02), 389--396, 2002.
  67. C.J. Butz and M.J. Sanscartier, Properties of Weak Conditional Independence, 3rd International Conference on Rough Sets and Current Trends in Computing (RSCTC02), 349--356, 2002.
  68. C.J. Butz and M.J. Sanscartier, Acquisition Methods for Contextual Weak Independence, 3rd International Conference on Rough Sets and Current Trends in Computing (RSCTC02), 339--343, 2002.
  69. C.J. Butz and M.J. Sanscartier, A Method for Detecting Context-Specific Independence in Conditional Probability Tables, 3rd International Conference on Rough Sets and Current Trends in Computing (RSCTC02), 344--348, 2002.
  70. C.J. Butz and M.J. Sanscartier, On the Role of Contextual Weak Independence in Probabilistic Inference, Fifteenth Canadian Conference on Artificial Intelligence (AI), 185--194, 2002.
  71. C.J. Butz, Exploiting Contextual Independencies in Web Search and User Profiling, World Congress on Computational Intelligence (WCCI), 1051--1056, 2002.
  72. C.J. Butz, Critical Remarks on Bayesian Network Libraries, 6th International Conference on Computer Science and Informatics (CSI02), 402-406, 2002.
  73. C.J. Butz, On Axiomatizing Probabilistic Conditional Independencies in Bayesian Networks, 1st Annual Asia-Pacific Conference on Web Intelligence (WI01), 131-135, 2001.
  74. S.K.M. Wong, C.J. Butz and D. Wu, On Undirected Representations of Bayesian Networks, ACM SIGIR Workshop on Mathematical/Formal Models in Information Retrieval (MF/IR), 52-59, 2001.
  75. S.K.M. Wong and C.J. Butz, A Bayesian Approach to User Profiling in Information Retrieval, ACM SIGIR Workshop on Mathematical/Formal Models in Information Retrieval (MF/IR), 50-56, 2000. (Also published in Technology Letters, Vol. 4, No. 1, 50-56, 2000.)
  76. S.K.M. Wong and C.J. Butz, A Comparative Study of Noncontextual and Contextual Dependencies, 12th International Symposium on Methodologies for Intelligent Systems (ISMIS00), 247-255, 2000.
  77. S.K.M. Wong and C.J. Butz, The Implication of Probabilistic Conditional Independence and Embedded Multivalued Dependency, 8th Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU00), 876--881, 2000.
  78. C.J. Butz and S.K.M. Wong, A Local Nest Property in Granular Probabilistic Networks, 5th Joint Conference on Information Sciences (JCIS00), Volume 1, Association for Intelligent Machinery, Inc., 158-161, 2000.
  79. S.K.M. Wong and C.J. Butz, Rough Sets for Uncertainty Reasoning, 2nd International Conference on Rough Sets and Current Trends in Computing (RSCTC00), 473-480, 2000.
  80. S.K.M. Wong and C.J. Butz, Contextual Weak Independence in Bayesian Networks, 15th Conference on Uncertainty in Artificial Intelligence (UAI99), Morgan Kaufmann, 670-679, 1999.
  81. C.J. Butz, S.K.M. Wong and Y.Y. Yao, On Data and Probabilistic Dependencies, IEEE Canadian Conference on Electrical and Computer Engineering (CCECE'99), IEEE Press, 1692-1697, 1999.
  82. C.J. Butz and S.K.M. Wong, Recovery Protocols in Multi-Agent Probabilistic Reasoning Systems, International Database Engineering and Applications Symposium (IDEAS'99), IEEE Press, 302-310, 1999.
  83. Y.Y. Yao, S.K.M. Wong and C.J. Butz, On Information-Theoretic Measures of Attribute Importance, 3rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PKDD'99), 133-137, 1999.
  84. S.K.M. Wong and C.J. Butz, A Probabilistic Network Versus Decision Rules, 6th International Conference on Rough Sets, Data Mining and Granular Computing (RSDMGrC98), Association for Intelligent Machinery, Inc., 310-315, 1998.
  85. S.K.M. Wong and C.J. Butz, A Method for Constructing the Dependency Structure of a Probabilistic Network, 7th Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU98), 1462-1469, 1998.
  86. S.K.M. Wong and C.J. Butz, Probabilistic Reasoning in a Distributed Multi-Agent Environment, 3rd International Conference on Multi-Agent Systems (ICMAS98), IEEE Press, 341-348, 1998.
  87. S.K.M. Wong and C.J. Butz, Equivalent Characterization of a Class of Conditional Probabilistic Independencies, 1st International Conference on Rough Sets and Current Trends in Computing (RSCTC98), 338-345, 1998.
  88. S.K.M. Wong, C.J. Butz and Y. Xiang, A Method for Implementing a Probabilistic Model as a Relational Database, 11th Conference on Uncertainty in Artificial Intelligence (UAI95), Morgan Kaufmann Publishers, 556-564, 1995.

Technical Reports

  1. C.J. Butz, H. Yao and S. Hua, A Join Tree Probability Propagation Architecture for Semantic Modelling, University of Regina, Computer Science Department, Technical Report CS-2004-10, November, 2004, ISBN 0-7731-0499-2.
  2. H. Yao, H. Hamilton and C.J. Butz, FD_MINE: Discovering Functional Dependencies in a Database Using Equivalences, University of Regina, Computer Science Department, Technical Report CS-02-04, August, 2002, ISBN 0-7731-0441-0.
  3. S.K.M. Wong, C.J. Butz and D. Wu, A Relational Knowledge System, University of Regina, Computer Science Department, Technical Report CS-01-04, January, 2001, ISSN 0828-3494, ISBN 0-7731-0417-8.
  4. S.K.M. Wong, C.J. Butz and D. Wu, On the Implication Problem for Probabilistic Conditional Independency, University of Regina, Computer Science Department, Technical Report CS-99-03, September, 1999, ISSN 0828-3494, ISBN 0-7731-0390-2.

Graduate Work

  1. Ph.D. thesis: The Relational Database Theory of Bayesian Networks.
  2. M.Sc. thesis: Probabilistic Reasoning Using an Extended Relational Data Model.
Graduate Student Supervision

  1. Congratulations to Jhonatan Oliveira for winning CAIAC's Award for Best Doctoral Dissertation on AI from a Canadian institution!
  2. Jhonatan was hired at Google!
  3. Getting ready to give a seminar at the University of Saskatchewan on research findings obtained with two of my students, Mr. Jhonatan Oliveira and Mr. Andre dos Santos.
  4. After the seminar, discussing the feedback and future papers with Jhonatan (front) and Andre (back). (It was -30C outside in Saskatoon.)
  5. Discussing research with Jhonatan and Andre in Brazil in 2014. (Much warmer, and why am I in a suit?)

Doctoral External Examiner
Name Thesis Title University Degree Year
Zhenyu Liao Improved Bayesian Network Structure Learning in the Model Averaging Paradigm Waterloo Ph.D. 2022
Maryam Ayat Sparse Bayesian Learning for Predicting Phenotypes and Identifying Influential Markers Manitoba Ph.D. 2018
Mahsa Naseri A Multi-functional Provenance Architecture: Challenges and Solutions Saskatchewan Ph.D. 2013
Jing Yan Multiagent Systems, Games and Learning from Structures York Ph.D. 2013
Wegdan Abdelsalam Roving-Object Modelling Framework for Location-Tracking Application Guelph Ph.D. 2011
Christina Manfredotti Modeling and Inference with Relational Dynamic Bayesian networks Milano-Bicocca, Italy Ph.D. 2009

Master's External Examiner

Name Thesis Title Degree Year
Puttipong Tantikhajorngosol Confidence Estimation of the Ratio of Variances of two Log-Normal Populations M.Sc. 2024
Dan Palmarin Cameron-Liebler Sets for 2-Transitive Groups M.Sc. 2020
Adam Kehler Performance of Dependent Bootstrap Confidence Intervals for Generalized Gamma Means M.Sc. 2018
Adam Gorr Eigenvalues of k-Uniform Hypergraphs M.Sc. 2017
Benjamin Perry High-throughput Functional Genomic Screening using Saturating Transposon Mutant Libraries and Next Generation Sequencing in Rhizobium Leguminosarum M.Sc. 2015
Qian Gao Semiparametric Methods for Regression Analysis with Missing Responses and Auxiliary Information M.Sc. 2008
Ryan Tifenbach Strongly Self-Dual Graphs M.Sc. 2007
Xiaoran Cao A Software Agent Community Model for the Shopping Assistant Agent System M.A.Sc. 2004

Current Graduate Students
Name Subject Area Degree Started Type
Kadence Meredith Probabilistic Circuits M.Sc. 2025 Thesis-based
Alejandro Santoscoy Probabilistic Circuits M.Sc. 2025 Thesis-based

Previous Graduate Students
Name Subject Area Degree Year Completed After Completion
Brandon Sasyniuk Bayesian networks M.Sc. 2024 TBD
Emily Haidl Bio-informatics M.Sc. (co-supervisor) 2021 Research Assistant at U Saskatchewan
Jhonatan Oliveira Sum-Product Networks Ph.D. 2020 Google
Andre dos Santos Sum-Product Networks Ph.D. 2020 Post-doctoral Fellow at the University of Alberta
Lobo Teixeira Sum-Product Networks M.Sc. 2020 Working in industry
Jun Wang Rough Sets M.Sc. 2020 Working in industry
Jhonatan Oliveira Bayesian networks M.Sc. 2015 Ph.D. Candidate at U Regina
Andre dos Santos Bayesian networks M.Sc. 2015 Ph.D. Candidate at U Regina
Wen Yan Structure and Semantics in Bayesian network Inference Ph.D. 2013 Working in Regina
Peter Lach Intelligent Tutoring Systems M.Sc. (co-supervisor) 2013 Working in Saskatchewan
Sultan Ahmed Bayesian networks M.Sc. 2013 Working in Regina
Jing Zeng Bayesian networks M.Sc. (co-supervisor) 2010 Working in Regina
Shan Hua Bayesian networks Ph.D. 2009 Regina Police Service
Junying Chen Bayesian networks M.Sc. 2008 Working in Regina
Ken Konkel Bayesian networks M.Sc. 2007 Working at iQmetrix
Hong Yao Structured Data Mining Ph.D. (co-supervisor) 2006 Working in Silicon Valley
Wen Yan Rough Set Flow Graphs M.Sc. (co-supervisor) 2006 Ph.D. candidate at the U of Regina
Fang Fang Databases M.Sc. 2006 Working in Montreal
Jidong Liu Probabilistic Reasoning M.Sc. 2005 Working at SaskTel
Shan Hua Intelligent Tutoring Systems M.Sc. (co-supervisor) 2004 Ph.D. Candidate at the U of Regina
Manon Sanscartier Probabilistic Reasoning M.Sc. 2003 Ph.D. Candidate at the U of Saskatchewan

NSERC Undergraduate Student Research Award (USRA) Supervision

Name Year
Ms. Camilla Lewis 2024
Ms. Kadence Meredith 2023
Ms. Kadence Meredith 2022
Mrs. Anna Norris 2022
Mr. Brandon Sasyniuk 2022
Ms. Yulia Shilova 2019
Ms. Elizabeth Rayner 2019
Mr. Jared Price 2018
Ms. Tori Verysdonk 2014
Ms. Rachel Popa 2011
Mr. Ryan Marcotte 2011
Mr. Kevin Williams 2010
Mr. Michael Berger 2009
Ms. Caleigh St. Onge 2007
Ms. Jillian Barkwell 2005
Ms. Sarah Ng 2004

Conference Organization
  1. General Co-Chair of 2013 Canadian Conference on Artificial Intelligence / Graphics Interface / Computer and Robot Vision Conference (AI/GI/CRV 2013).
  2. General Co-Chair of 2012 Canadian Conference on Artificial Intelligence / Graphics Interface / Computer and Robot Vision Conference (AI/GI/CRV 2012).
  3. Program Co-Chair of Uncertain Reasoning at FLAIRS 2012 (UR'12).
  4. Program Co-Chair of Uncertain Reasoning at FLAIRS 2011 (UR'11).
  5. Program Co-Chair of Canadian Artificial Intelligence Conference 2011 (AI'11).
Community Outreach

Overviewing Artificial Intelligence at an Elementary School

Here are a few pics of some of the students in the 7/8 class at Deshaye Elementary School.
  1. Intro
  2. Some of the students
  3. NASA uses AI
  4. A curious, young scientist
  5. Remind me in which grade students stop asking questions

Escorting a kindergarten class to the Royal Saskatchewan Museum

A fun day learning about animals and nature. Take a look.
  1. Class
  2. Moose
  3. Bear den
  4. Wolves
  5. Caribou
  6. Beaver den
  7. Monarch butterflies
  8. Rainforest
  9. Summer vs Winter fur
  10. T-Rex

Regina Science Fair Judge

I always enjoy serving as a judge at the Regina Science Fair. Lots of cool exhibits every year!
  1. Overview
  2. A few projects
  3. Before the students arrive
  4. The students in action
  5. The main organizer, Dr. Pierre Ouimet
Past Courses
  • 2024F - CS110-001 Programming and Problem Solving for Natural Sciences
  • 2024S - CS838-001 Uncertain Reasoning in Artificial Intelligence
  • 2024W - CS475-001 Database Management Systems
  • 2024W - CS875-001 Database Management Systems
  • 2023F - CS110-001 Programming and Problem Solving for Natural Sciences
  • 2022W - CS110-001 Programming and Problem Solving for Natural Sciences
  • 2021F - CS838-001 Uncertain Reasoning in Artificial Intelligence
  • 2021W - CS110-001 Programming and Problem Solving for Natural Sciences
  • 2020F - CS475-001 Database Management Systems
  • 2020F - CS875-001 Database Management Systems
  • 2020W - CS110-001 Programming and Problem Solving for Natural Sciences
  • 2019F - CS838-001 Uncertain Reasoning in Artificial Intelligence
  • 2019W - CS110-001 Programming and Problem Solving for Natural Sciences
  • 2018F - CS475-001 Database Management Systems
  • 2018F - CS875-001 Database Management Systems
  • 2018W - CS110-001 Programming and Problem Solving for Natural Sciences
  • 2017S - CS838-001 Uncertain Reasoning in Artificial Intelligence
  • 2017W - CS110-001 Programming and Problem Solving for Natural Sciences
  • 2016S - CS875-001 Database Management Systems
  • 2016W - CS110-001 Programming and Problem Solving for Natural Sciences
  • 2015S - CS838-001 Uncertain Reasoning in Artificial Intelligence
  • 2014F - CS110-001 Programming and Problem Solving for Natural Sciences
  • 2013F - CS110-001 Programming and Problem Solving for Natural Sciences
  • 2012F - CS110-001 Programming and Problem Solving for Natural Sciences
  • 2012W - CS110-001 Programming and Problem Solving for Natural Sciences
  • 2011F - CS110-001 Programming and Problem Solving for Natural Sciences
  • 2011F - CS375-001 Introduction to Database Systems
  • 2011W - CS838-001 Uncertain Reasoning in Artificial Intelligence
  • 2010F - CS375-001 Introduction to Database Systems
  • 2010S - CS110-040 Programming and Problem Solving for Natural Sciences
  • 2010W - CS110-001 Programming and Problem Solving for Natural Sciences
  • 2009F - CS110-001 Programming and Problem Solving for Natural Sciences
  • 2009F - CS375-001 Introduction to Database Systems
  • 2009W - CS475-001 Advanced Topics in Database Systems
  • 2008F - CS110-003 Programming and Problem Solving for Natural Sciences
  • 2008F - CS838-001 Uncertain Reasoning in Artificial Intelligence
  • 2007W - CS110-002 Programming and Problem Solving for Natural Sciences
  • 2007W - CS475-001 Advanced Topics in Database Systems
  • 2006S - CS110-070 Programming and Problem Solving for Natural Sciences
  • 2006W - CS838-001 Uncertain Reasoning in Artificial Intelligence
  • 2005F - CS110-001 Programming and Problem Solving for Natural Sciences
  • 2005F - CS130-001 Programming and Problem Solving for Engineering
  • 2005W - CS470-001 Advanced Topics in Database Systems
  • 2005W - CS130-001 Programming and Problem Solving for Engineering
  • 2004S - CS890CQ Advanced Models for Probabilistic Reasoning
  • 2004S - CS110-070 Programming and Problem Solving for Natural Sciences
  • 2004W - CS375-001 Introduction to Database Systems
  • 2004W - CS838-001 Uncertain Reasoning in Artificial Intelligence
  • 2003S - CS110-070 Programming and Problem Solving for Natural Sciences
  • 2003S - CS490-BW 070 Introduction to Bayesian networks
  • 2003W - CS130-001 Programming and Problem Solving for Engineering
  • 2002F - CS110-002 Programming and Problem Solving for Natural Sciences
  • 2002F - CS375-001 Introduction to Database Systems
  • 2002F - CS490-BO Advanced Database Design
  • 2002S - CS890-BQ Semantic Database Models
  • 2002S - CS490-BO Advanced Database Design
  • 2002W - CS838-001 Uncertain Reasoning in Artificial Intelligence
  • 2001F - CS375-001 Introduction to Database Systems
  • 2000F - CSI3317A Introduction to Database Systems (University of Ottawa)
  • 2000F - CSI3317B Introduction to Database Systems (University of Ottawa)
  • 1999W - CS375-033 Introduction to Database Systems
  • 1998F - CS375-033 Introduction to Database Systems
  • 1998S - CS375-001 Introduction to Database Systems
  • 1998W - CS375-002 Introduction to Database Systems
  • 1997S - CS375-001 Introduction to Database Systems
  • 1997W - CS375-001 Introduction to Database Systems