27th Canadian Conference on Artificial Intelligence
May 6-9, 2014, Montréal, Québec, Canada
AI 2014 will include a technical program consisting of presentations of research papers, invited speakers, and tutorials describing the state-of-the-art in artificial intelligence. A Graduate Student Symposium will occur on the first day of the conference. AI 2014 will feature the following distinguished invited speakers.
In this talk we will review text mining, a "hot" area of current AI research. There is little doubt that text is an important kind of data, and that it calls for special analysis and information extraction techniques. It has been stated that text data constitutes 80% of the overall data volume. In the last fifteen years, Machine Learning and Applied Computational Linguistics have come together to significantly contribute to intelligent text mining. In this talk we will review the history of the field and some of its more recent accomplishments, e.g. selected modern text mining techniques such as Conditional Random Fields and Latent Dirichlet Allocation. We will speculate where the field may be going. In particular, we will ponder the Big Data argument, suggesting that the ability to process huge data (and text) repositories limits, or even eliminates, the need for knowledge-intensive methods.
Stan Matwin is Canada Research Chair (Tier 1) in Visual Text Analytics at Dalhousie University. His other honours include Fellow of the European Coordinating Committee on Artificial Intelligence, Fellow of the Canadian Artificial Intelligence Association, and Ontario Champion of Innovation. He has held academic positions at many universities in Canada, the U.S., Europe, and Latin America including the University of Guelph, Acadia University, and the University of Ottawa where in 2011 he was named a Distinguished University Professor (on leave). In 2013, he moved to Dalhousie University, where he is a CRC and Director of the Institute for Big Data Analytics. Stan is recognized internationally for his work in text mining, applications of machine learning, and data privacy. He is author or co-author of more than 250 research papers. He is a former president of the Canadian Artificial Intelligence Association (CAIAC) and of the IFIP Working Group 12.2 (Machine Learning). Stan has significant experience and interest in innovation and technology transfer and he is one of the founders of Distil Interactive Inc. and Devera Logic Inc.
There is significant pressure to link and share health data for research, public health, and commercial purposes. However, such data sharing must be done responsibly and in a manner that is respectful of patient privacy. Secure multi-party computation (SCM) methods present one way to facilitate many of these analytic purposes. In fact, in some instances SCM is the only known realistic way allow some of these data disclosures and analyses to happen (without having to change the law to selectively remove privacy protections). This talk will describe two recent real-world projects where SMC was applied to address such data sharing concerns. The first was to measure the prevalence of antimicrobial resistant organism (e.g., MRSA) infections across all long term care homes in Ontario. A SMC system was deployed to collect data from close to 600 long term care homes in the province and establish a colonization and infection rate baseline. The second project pertains to securely linking large databases to allow de-duplication and secure look-up operations without revealing the identity of patients. This system performs approximate matching while maintaining a constant growth in complexity. In both of these cases a number of theoretical and engineering challenges had to be overcome to scale SCM protocols to operate efficiently and to transition them from the laboratory into practice.
Khaled El Emam is founder and CEO of Privacy Analytics Inc., a software company which develops de-identification tools for hospitals and registries to manage the disclosure of health information to internal and external parties. Khaled is also an Associate Professor at the University of Ottawa, Faculty of Medicine and the School of Information Technology and Engineering, a senior investigator at the Children's Hospital of Eastern Ontario Research Institute, and a Canada Research Chair in Electronic Health Information at the University of Ottawa. His main area of research is developing techniques for health data anonymization and secure disease surveillance for public health purposes. Previously, Khaled was a Senior Research Officer at the National Research Council of Canada, and prior to that he was head of the Quantitative Methods Group at the Fraunhofer Institute in Kaiserslautern, Germany. He has co-founded two companies to commercialize the results of his research work. In 2003 and 2004, he was ranked as the top systems and software engineering scholar worldwide by the Journal of Systems and Software based on his research on measurement and quality evaluation and improvement, and ranked second in 2002 and 2005.