Keynote Speaker---Dr. A. Fazel Famili


Data Scientist and Data Analytics Consultant, University of Ottawa, Canada

Biography: Dr. A. Fazel Famili is a Data Scientist and a Data Analytics Consultant working in various domains such as Engineering and Life Sciences. He worked as a Research Scientist and Group Leader at the National Research Council of Canada in Ottawa, Canada from 1984-2015. Prior to joining NRC, he worked in industry for 3 years. His interests include data mining, pattern recognition, machine learning, bioinformatics, and knowledge discovery from on-line or historical data. He has lectured in a number of Research Institutes in Canada, Europe, Far East, South Africa and South/Central America. Dr. Famili is founding Editor-in-Chief of the IDA Journal (Intelligent Data Analysis, a refereed scientific journal, established in 1996), published bi-monthly. He is also affiliated with the University of Ottawa, Canada. He has edited two books, published over 50 articles in data mining and Artificial Intelligence and has a US data mining patent.

Speech Title: Searching for patterns in real-world data: From problem understanding to validation

Abstract: Our advancements and achievements in information science and technology over the last 10-20 years, have been the prime motivation for various industries to accumulate huge amounts of data from all levels of their operation. This has resulted in creating large databases for which much of the useful insights are sometimes hidden and untapped. Many attempts have been made in the last 10-20 years to apply systematic methodologies in order to build knowledge discovery and management applications. However, establishing and managing a real-world data mining project is not a trivial task. This is simply due to the fact that majority of industries (such as engineering and life sciences, etc.) have gone through an evolving paradigm where data collection, problem understanding and validation of discovered patterns have become more complex.

Today's knowledge discovery and pattern recognition from data can be classified in several ways: (i) data mining on engineered systems (e.g. complex equipment) or systems designed by nature (e.g. life sciences), (ii) explanatory or predictive data mining, (iii) data mining from static data (e.g. data warehouse) or dynamic data (e.g. data streams). In this talk, we will first provide a brief overview of data in the real world and explain some of the challenges that knowledge discovery projects have encountered. We will then present two case studies (an engineering and a medical application) that cover certain aspects of knowledge discovery process and how fuzzy systems can play a key role in the entire data mining paradigm. Actual scientific and business examples presented in this talk will illustrate proven case studies designed, implemented and evaluated by domain experts. We also demonstrate how our case studies can lead to real-world applications and even tools that could be deployed for better management of data from today’s data rich environments.