Abstract

Data streams are ubiquitous in society, and extracting accurate and timely knowledge from these repositories is one of the most significant challenges that we face today. Indeed, in an increasingly fast-paced world, many of today’s pressing problems present themselves in the form of ubiquitous streams. Learning in such evolving domains presents many challenging and interrelated problems, due to the change in data distributions and feature importance over time, as well as the added complexities when learning in the presence of emerging concepts and highly skewed data. Applications are numerous and include detecting intrusion and phishing attacks in cyberspace, monitoring environmental patterns for emergency awareness and responsiveness, and preventive social media analysis. This talk highlights our current research on adaptive algorithms designed to construct accurate, trustworthy models that seamlessly adapt as data streams ebb and flow.

Short Bio

Herna Viktor is Full Professor at the School of Electrical Engineering and Computer Science (EECS) at the University of Ottawa and director of the Applied Artificial Intelligence group at the same university. Her research focuses on discovering knowledge from data, with an emphasis on advanced machine learning algorithms to extract a semantic meaning from Big Data. She is the author of more than 150 magazine articles, conference articles and book chapters. The final results of her investigation were applied in several and diverse domains, including the study of pediatric patients with anemia, in collaboration with the Hospital for Sick Children in Toronto; exploring media discourse in the Alberta tar sands debate; sentiment analysis and opinion mining in elections; and, more recently, a study on Canadians' oral health.

About Talks@DCC 

The mission of the Talks@DCC seminars is to bring together researchers and students, to foster discussions and promote scientific awareness and collaboration.

Participate: attend and propose your talk.