Eileen Ni

Eileen A. Ni

PhD Candidate
Data Mining & Business Intelligence
Department of Computer Science
The University of Western Ontario
London, Ontario N6A 5B7, Canada

Email: ani@uwo.ca

Technical Skills

  • Familiar with softwares, including SAS, Qlickview, SPSS
  • Competent with data-mining software and associated platforms, such as WEKA, JAVA, Eclipse, etc.
  • Competent with JAVA, C++, C, Python, Unix, Windows, Web server development
  • Familiar with Database, SQLServer 2008, MySQl, SQL, T-SQL

BI & IT Project Experience

  • 6-year experience in BI, data mining and related IT skills (applying data mining techniques, such as classification, clustering, linear regression and principal component analysis, etc, and related software to build models for prediction or forecasting to support decision-making or strategic planning.)
  • 5-year experience in SQL Database and data warehouse


  • Direct Marketing Data Analysis:

I build models for predicting the buying behavior of customers in a major insurance company in Canada with data mining techniques (such as classification and clustering). The buying rate with our direct-marking analysis was 4 times higher than previous methods. Techniques used: WEKA, JAVA, Eclipse, SQL Server, SQL

  • Image Search Engine:

I am in charge of crawling web images from Internet, extracting surrounding text, building a web tagging system for images. Techniques used: Database, SQL, Python, JSP, Servlet, Javascript, Html, AJAX.

  • Supermarket Data Analysis:

I built multi-level association models with data mining techniques to analyze the relations among the items in the supermarket. With this, users can select the items they are interested to mine the association rules among them. They also can drill down and build association rules of different levels. Techniques used: WEKA, JAVA, Eclipse, SQL Server, SQL

Selected Publications

  1. E. A. Ni and C. X. Ling. Direct Marketing with Fewer Mistakes. Advanced Data Mining and Applications, 2011.
  2. E. A. Ni and C. X. Ling. Active Learning with c-Certainty. Proceedings of the 16th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2012.
  3. E. A. Ni and C. X. Ling. Learning with Guaranteed Label Quality. 2011 10th International Conference on Machine Learning and Applications Workshops.
  4. E. A. Ni, C.X. Ling. Supervised Learning with Minimal Effort. Proceedings of the 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2010.
  5. J. Du, Eileen. A. Ni and C.X. Ling. Adapting Cost-sensitive Learning for Reject Option. Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM), 2010.
  6. Eileen. A. Ni, C.X. Ling. Self-Directed Learning. Proceedings of International Conference on Artificial Intelligence and Education (ICAIE), 2010.
  7. S. Sheng, C. X. Ling, E. A. Ni and S. Zhang. Test Strategies for Cost-Sensitive Decision Trees. Proceedings of 21st National Conference on Artificial Intelligence (AAAI-06), July 16-20, 2006, Boston, Massachusetts, USA.
  8. E. A. Ni, X. Zhu, C. Zhang. Any-Cost Discovery: Learning Optimal Classification Rules The 18th Australian Joint Conference on Artificial Intelligence (2005) .

Honors and Awards

  • Second place, Data mining Session at UWO Research in Computer Science, 2012

Teaching Assistant

  • The University of Western Ontario 2008 - 2012

CS3346: Artificial Intelligence I (Fall 2010)

Brief answer for assignment1: answer_assign1_1_.pdf

Research Assistant

  • The University of Western Ontario 2008 - 2012

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