[REMINDER] We welcome you to join us for our next Information Management (IM) Public Lecture. Please share with your network. Apologies for cross-posting. The Information Management Public Lectures give attention to exciting advances in research and professional practice. The topics are diverse reflecting the importance and global extent of Information Management in today's society. The lectures are open to all members of the Dalhousie campus and surrounding community. Click here <https://www.dal.ca/faculty/management/school-of-information-management/news-events/information-managementpubliclectureseries.html> for the full schedule. This lecture will NOT be recorded. Big Data Analytics and Artificial Intelligence Techniques to make Better Management Systems Jamal Shahrabi Amirkabir University of Technology, Iran Lecture Details Thursday, September 13th, 2018 1:30pm Room 5053, Kenneth C. Rowe Management Building 6100 University Ave Abstract: Big data analytics is the process of examining large and varied data sets to discover hidden patterns, unknown correlations, market trends, customer knowledge and other useful information that can help organizations make more-informed business decisions. All industries now are facing with a large amount of data and complex management issues with a much more competition than before. The most important benefits of big data analytics compare to classical analytical methods are speed and efficiency. Few years ago a business would have gathered data, run traditional analytics and provided information that could be used for future decisions, today that business can identify insights for immediate decisions by smart management systems. Meanwhile all organizations and industries are involving with a broad range of decision making criteria and multiple different internal goals and targets that make decision taking difficult. In this situation classical analytical methods do not work anymore and Multi Agent Systems (MAS) are needed. Multi Agent Ensemble Learning Systems are used in a variety of domains for making collaborative smart decision support systems by discovering a solution by agents on their own, using learning. The most important part of the problem is how the agents will learn independently and then how they will cooperate to establish the common task. Biography: Dr. Jamal Shahrabi received his PhD from Dalhousie University. He is a faculty member of Industrial Engineering and Management Systems faculty at Amirkabir University of Technology (AUT). His research interests lie at the intersection of data science (particularly data analysis, artificial intelligence, machine learning & data mining) and Management and Marketing (particularly smart management, operations management, management information systems, business intelligence, customer knowledge discovery, customer relationship management & smart marketing,). His research contributions has been to develop the efficient machine learning and data mining models to solve the real management and marketing problems by designing smart models and systems for smart decision making and smart management. He has been succeed to make a synergy of university and industry to solve the industry management problems and provide the opportunity of involvement of students in real industry issues. He has managed 9 big size industry projects so far. He has published two book chapters, 13 books in Persian, 136 conference papers & 34 outstanding ISI journal papers with 865 Citations So far. Graduating 73 Master and 5 PhD students under his supervision and teaching several different bachelor, master and PhD courses in the field of management and marketing is his honor.