Quan Sun

I am a data scientist and machine learning developer.

Email: quan.sun.nz@gmail.com and my LinkedIn profile

What's new

  • 02/2017 Started at Movio
  • 05/2016 11Ants was awarded the Innovative Software Product of 2016 by Microsoft New Zealand
  • 01/2016 AlphaGo defeated the European Go champion!
  • 12/2015 My first Coursera certificate(Computational Investing, Part I).
  • 03/2015 11Ants Analytics was acquired by Aimia and Air New Zealand.
  • 10/2013 The Fantail ml kit is available for download now.

Research Interests

My PhD research focused on recommender systems for meta-learning. 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 applied mathematics in general, including subfileds of AI, Machine Learning and Statistics. The current list includes:
  • automatic differentiation in deep learning
  • federated learning
  • recommender system/learning to rank

Professional Activities

Reviewing: IEEE Transactions on Reliability, Springer Machine Learning Journal, Elsevier Information Sciences, NIPS 2016/2017, ICML 2018

Some old code

  • 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
  • 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 :-)

Publications

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

Honors & Awards

  • 2016 - Team member, the Innovative Software Product of 2016 awarded by Microsoft NZ
  • 2013 - Waikato Faculty of Computing and Mathematical Sciences PhD Study Award
  • 2011 - kaggle top 10
  • 2009/10 - 1st place, University of California, San Diego/FICO Data Mining Contest
  • 2009 - The TechNZ Postgraduate Award