Tracking The Mind: Measuring Social Media Behaviour
Professor Ding received her BSc. and MSc. degree from Nanjing University and PhD degree from the National University of Singapore. She joined the Department of Computer Science at Ryerson University in 2003, where she holds the rank of professor.
Her research interests are in the areas of service computing, data analytic services in the cloud, recommendation systems, social network analysis, and Quality of Services.
Currently, a primary focus of her work is on cloudbased service selection and recommendation, and behavior analytics for social networks, such as Facebook. Her current research projects, funded by Natural Sciences and Engineering Research Council of Canada and Canada Foundation for Innovation, examine quality recommendation for software service provisioning and QoS-based web service selection and ranking.
Organic Synthesis: Creating Efficient Solutions
AI is a broad activity that includes several exciting research areas. One of the key issues is how creative human problem solving can be automated within the available computational resources. Mikhail Soutchanski works in this direction using a principled approach to reasoning about actions. Using this approach problem solving can be represented symbolically in a logical language. Soutchanski works both on foundational problems in AI and on applications of AI to solving the organic synthesis problem. The organic synthesis problem is about finding a sequence of reactions that can produce the target molecule from a given set of initial molecules. Since molecules can be represented as graphs and reactions as graph transformations, this problem is readily representable in terms of symbol manipulation. Efficient algorithms and data structures for solving some of the feasible instances of this problem can have a number of practical applications ranging from designing new medicines to developing Web based tutoring systems that help students to learn how the organic synthesis problem can be solved.