Dr. Jamal Shahrabi, PhD, PEng

Big Data Analytics and Multi Agent Ensemble Learning Approach to Make the Smart Decision Support Systems

Data Scientist
CEO & President of Data Science Marketing Company

Date: January 27, 2021
Time: 1:00-2:00 PM
Venue: Online Event


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 solution is just using the new technologies of big data analytics to manage their organization better by making the smart management system. 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 department based goals and targets that make decision taking process so difficult. In this situation classical analytical methods do not work anymore and Multi Agent Systems (MAS) are needed. Multi Agent Ensemble Learning Systems are rapidly 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.

Speaker Biography

Dr. Jamal Shahrabi received his PhD from Industrial Engineering Departement of Dalhousie University. He used to work as a faculty member of Industrial Engineering  faculty at Amirkabir University of Technology (AUT) for 21 years. His research interests lie at the intersection of data science (particularly big data analysis, artificial intelligence, machine learning & data mining) and Industrial Engineering, Management and Marketing (particularly smart marketing, smart management, decision support systems, management information systems, business intelligence, customer knowledge discovery, customer relationship management & ...). His research contributions has been to develop the efficient machine learning and data mining models to solve the real industrial, 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 several big size industry projects and 10 annual data mining conferences. He has published two book chapters, 13 books in Persian, 136 conference papers & 48 outstanding ISI journal papers with 1525 Citations So far. Graduating 85 Master and 7 PhD students under his supervision and teaching several different bachelor, master and PhD courses in the field of industrial engineering and data science is his honor. 

Contact Person:
Prof. Floris Goerlandt
email: floris.goerlandt@dal.ca

General Enquiry:
Ms. Tara Parker
Tel: 902.494.3281
email: ieng@dal.ca