Monday November 21, 2016 at 11:00 a.m. Dr. Ayse Basar Bener, professor and director of Data Science Laboratory at Ryerson University, will be presenting “AI-Based Software Defect Predictors: Applications and Benefits and Lessons Learned”.
Speaker: Dr. Ayse Basar Bener
Professor, Director of Data Science Laboratory, Department of Mechanical and Industrial Engineering
Director of Big Data, Office of Provost and Vice President Academic
Day & Time: Monday, November 21, 2016
11:00 a.m. – 12:00 p.m.
Location: KHE 225, Ryerson University, 340 Church Street, Toronto
Contact: Maryam Davoudpour
Organizer: WIE, Magnetics, Measurement/Instrumentation-Robotics, Computer Science Department of Ryerson University
Abstract: Software analytics guide practitioners in decision making throughout the software development process. In this context, prediction models can help managers efficiently organize their resources and identify problems by analyzing patterns on existing project data in an intelligent and meaningful manner. In this talk I will share my experiences building and deploying AI (machine learning) models in software organizations over 15 years. We have encountered similar data analytics patterns in diverse organizations and in different problem cases. I will give examples from deployed projects and discuss these patterns following a “software analytics” framework: problem identification, data collection, descriptive statistics, and decision making.
Biography: Dr. Ayse Basar Bener is a professor and the director of Data Science Laboratory (DSL) in the Department of Mechanical and Industrial Engineering, Ryerson University. She is the director of Big Data in the Office of Provost and Vice President Academic at Ryerson University. She is a faculty research fellow of IBM Toronto Labs Centre for Advance Studies, and affiliate research scientist in St. Michael’s Hospital in Toronto. Her current research focus is big data applications to tackle the problem of decision-making under uncertainty by using machine learning methods and graph theory to analyze complex structures in big data to build recommender systems and predictive models. She is a member of AAAI, INFORMS, AIS, and senior member of IEEE.