COMMERCIAL INDUSTRIAL MATHEMATICS ACTIVITIES

Schedule - April 25

Speakers Abstracts

Dr. Susan Kular
Databases, Business Operations and CRM

To achieve results from 121 marketing, the following aspects of business operations are important:
- Acquisition of relevant and accurate databases
- To have the business operation systems in place to implement database marketing
- To ensure that the business infrastructure supports CRM paradigm, i.e. customer-focused marketing

Dr. Bobby Siu
Colour Your World: Why Multicultural Marketing Makes Sense

Canada has been going through some demographic shifts in the past few decades. The presentation examines some population and consumption statistics and notes the value of developing marketing strategies for multicultural groups.

Biography:
For over 20 years, Dr. Bobby Siu he has been a leading researcher and manager in the field of ethnocultual relations and diversity management. He provides consulting, training and research services to business organizations in developing and executing their multicultural marketing and diversity strategies.

He has a Ph.D. in sociology from Carleton University and has held academic appointments at McMaster University, Queen's University, Ryerson Polytechnic University, University of Western Ontario and York University. In addition, he has held various senior management posts with the Government of Ontario where he assumed responsibility for a wide range of projects involving multicultural communities.

Through his business activities and sitting on the boards of various organizations, Dr. Siu has extensive linkages to the ethnocultural communities. His views on diversity and marketing have been quoted in Canadian Business and Strategy. He has extensive publications on intercultural and diversity including Canadian Ethnic Studies, Canadian HR Reporter, Marketing Magazine, Strategy, and the award-winning Toronto Region Business and Market Guide.

Prof. JianHong Wu
Projctive Clustering of Data Sets in Large Dimensional Spaces

Projective clustering seems to be the only realistic approach in pattern recognition and feature detection of data sets in high dimensional spaces, as points in such data sets are usually sparsely distributed in the full space. The essential difficulty arises as the set of dimensions and the set of data points have to be found SIMULTANEOUSLY so that the points are corollated with respect to the set of dimensions. We report some of our recent progress in the design and implementation of a neural network architecture, called Projective Adaptive Resonance Theory, for projective clustering. The algorithm and numerical simulations will be described, together with some detailed comparisons with existing algorithms for clustring of large data sets in high dimensional spaces.

Bioraphy:
Jianhong Wu is a Canada Research Chair in Applied Mathematics (Tier I) and a Professor of Mathematics at York University. He is the author or coauthor for over 140 papers and 4 monographs in a number of scientific areas including delay differential equations, dynamical systems, mathematical biology, neural networks and data analysis and he is serving on the editorial boards of 7 international journals. He has also been leading a research team and the development of the newly founded Laboratory for Industrial and Applied Mathematics (LIAM)