My PhD research focused on using meta-learning to recommend data mining/machine learning algorithms and solutions to a given data problem. My thesis includes both theoretical and empirical contributions to machine learning and data mining in general, including new ranking, new feature generation, ensemble learning, evolutionary computation and meta-learning methods and algorithms. My supervisors were Professor Bernhard Pfahringer and Dr Michael Mayo. Assoc. Prof Russel Pears and Prof. Pavel Brazdil were the examiners of my thesis.
I'm interested in CS and applied mathematics in general, including subfileds of AI, Machine Learning and Statistics. The current list includes:

• ranking data analysis
• metalearning (algorithm/parameter recommendation)
• recommender system/learning to rank
• classification/regression ensemble learning
• decision tree/rule algorithms
• optimisation techniques
• cloud-based computing architecture/large-scale Web applications

I'm also interested in business analytics, econometrics and social sciences...

• Fantail (Fantail is a collection of machine learning algorithms for ranking prediction, multi-target regression, label ranking and metalearning related data mining tasks.)
• ART Forests 0.1 for Windows.
• BES and NNLS ensemble pruning.
• Click here if you are interested in my solutions for the 2009/2010 UCSD data mining contests. (Binary classification solutions for imbalanced datasets (credit card fraud detection))
• My solution to the "Predicting the outcome of grant applications" competition on kaggle. (my Kaggle profile)
• Click here for a WEKA-based stochastic gradient boosting algorithm for regression. (an algorithm used for the Bond Trade Price competition on kaggle)
• vbWeka a wee project created in 2005 :-)

Journal/Conference Papers

• Quan Sun and Bernhard Pfahringer. Hierarchical Meta-Rules for Scalable Meta-Learning. In Proceedings of the 13th Pacific Rim International Conference on Artificial Intelligence (PRICAI'14), Gold Coast, Queensland, Australia, 2014. (pdf | publisher link | slides)
• Michael Mayo and Quan Sun. Evolving Artificial Datasets to Improve Interpretable Classifiers. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC'14), Beijing, China, pp. 2367--2374, 2014. (pdf | publisher link)
• Quan Sun and Bernhard Pfahringer. Pairwise Meta-Rules for Better Meta-Learning-Based Algorithm Ranking. Machine Learning, 93(1):141-161, Springer US, 2013, DOI: 10.1007/s10994-013-5387-y (pdf | publisher link | software & supplementary materials | slides | poster)
• Quan Sun, Bernhard Pfahringer and Michael Mayo. Towards a Framework for Designing Full Model Selection and Optimization Systems. In Proceedings of the 11th International Workshop on Multiple Classifier Systems (MCS'13), Nanjing, China, LNCS 7872, pp. 259--270. Springer, Heidelberg, 2013. (pdf | publisher link | slides)
• Quan Sun and Bernhard Pfahringer. Bagging Ensemble Selection for Regression. In Proceedings of the 25th Australasian Joint Conference on Artificial Intelligence (AI'12), Sydney, Australia, pages 695--706. Springer, 2012. (pdf | publisher link | slides | BESTrees package)
• Quan Sun, Bernhard Pfahringer and Michael Mayo. Full Model Selection in the Space of Data Mining Operators. In Proceedings of the ACM Conference on Genetic and Evolutionary Computation (GECCO'12), Philadelphia, United States, 2012. (publisher link | poster)
• Quan Sun and Bernhard Pfahringer. Bagging Ensemble Selection. In Proceedings of the 24th Australasian Joint Conference on Artificial Intelligence (AI'11), Perth, Australia, pages 251--260. Springer, 2011. (pdf | publisher link | slides | software | datasets)

My Theses

• Quan Sun. Meta-learning and the full model selection problem. PhD thesis, Department of Computer Science, University of Waikato, Hamilton, NZ, 2014. (pdf)
• Quan Sun. Sampling-based Prediction of Algorithm Runtime. Master's thesis, Department of Computer Science, University of Waikato, Hamilton, NZ, 2009. (pdf) Supervised by Associate Professor Eibe Frank

@NZCSRSC (not archival publications, equivalent to working papers)

• Quan Sun. Bagging Ensemble Selection for Regression Problems. In Proceedings of the 11th New Zealand Computer Science Research Student Conference (NZCSRSC'13), Hamilton, New Zealand, 2013.
• Quan Sun. Full Model Selection and Optimization. In Proceedings of the 10th New Zealand Computer Science Research Student Conference (NZCSRSC'12), Dunedin, New Zealand, 2012.
• Quan Sun. Getting Even More Out of Ensemble Selection. In Proceedings of the 9th New Zealand Computer Science Research Student Conference (NZCSRSC'11), Palmerston North, New Zealand, 2011. (paper)
• Quan Sun. Algorithm Runtime Prediction. In Proceedings of the 7th New Zealand Computer Science Research Student Conference (NZCSRSC'09), Auckland, New Zealand, 2009. (short paper thesis pdf)
• Quan Sun. The Life of a Session Result. In Proceedings of the 6th New Zealand Computer Science Research Student Conference (NZCSRSC'08), Christchurch, New Zealand, 2008. (paper)

Reviewing: IEEE Transactions on Reliability, Springer Machine Learning Journal, Elsevier Information Sciences

The game of Go interests me a great deal. I'm keen to the research on human-like Go-playing/teaching robot, e.g., sitting in front of a Go board and wearing a kimono, certainly she/he should be able to pick up stones from the bowls. I'm an amateur 3 +- 1 dan player depending on how much coffee I had :-)

Honors & Awards

• 2014 - Higgs Boson Machine Learning Challenge: 28th/1785 teams
• 2013 - Waikato Faculty of Computing and Mathematical Sciences PhD Study Award
• 2011 - 1st runner up, the "grant application outcome prediction" competition on kaggle
• 2010 - 1st place, University of California, San Diego/FICO Data Mining Contest
• 2010 - 5th place, Rough Sets and Current Trends in Computing Conference Discovery Challenge (Advanced Track)
• 2010 - 2013 University of Waikato Doctoral Scholarship
• 2009 - 1st place, University of California, San Diego/FICO Data Mining Contest
• 2009 - The TechNZ Postgraduate Award
• 2008 - 2009 University of Waikato Masters Research Scholarship

Links

• My LinkedIn Profile
• Weka machine learning software
• Machine Learning and related area blogs
• My blog on the game of GO
• Enjoy the new Ted Goff cartoon which looks at preparations for the Big Data Flood.