Early Detection of Emerging Technologies Using Machine Learning and Burst Detection

Ali Ghaemmaghami is a PhD student at Concordia University with expertise in applying data analytics solutions across various disciplines, including finance, marketing, and scientometrics. He is currently focusing on using statistical and machine learning approaches to detect emerging technologies. Ali is pursuing a PhD in Information Systems Engineering, and but has obtained both a Master’s and a Bachelor’s degree in Industrial Engineering. 

Andrea Schiffauerova is a Professor at Concordia Institute for Information Systems Engineering at Concordia University. She obtained her PhD and MEng degrees in Industrial Engineering from École Polytechnique de Montréal, Canada, and BEng from Silesian University, Czech Republic. Her area of expertise encompasses management of science and technology, scientometrics and economics of innovation. Her current research involves using artificial intelligence and big data analytics to explore innovation ecosystems, while focusing on various aspects such as the dynamics of innovation networks and collaboration, gender-specific patterns and disparity in science, and early detection and transformation of emerging science into technology.

Dr. Ashkan Ebadi is a multidisciplinary applied data science researcher with expertise in artificial intelligence (AI), machine learning, deep learning, and graph analytics. He received his Ph.D. in information systems engineering with an emphasis on AI-based decision support systems. He also carried a two-year postdoctoral fellowship in health informatics at the University of Florida (USA). He is currently a Senior Research Officer at the National Research Council Canada (NRC), Adjunct Assistant Professor at the University of Waterloo, Affiliate Assistant Professor at Concordia University (Canada), and Senior Member of the Institute of Electrical and Electronics Engineers (IEEE) organization. Ashkan has intensive academic and industrial experience in the design and implementation of intelligent data-driven solutions. His professional experience covers the entire life-cycle of the data science pipeline, from (business) problem definition to scalable big data analytics applications. His research aims to leverage advanced analytics and machine learning to solve complex real-life problems in various domains, e.g., healthcare, and social sciences..