Critical actors in the collaborative ecosystem

Polytechnique Montréal

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<h3>Informations :</h3><h3> <a href="https://www.4point0.ca" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">www.4point0.ca</a></h3>

<p>Présentation et discussion en compagnie de<strong> Mahsa Noorinajafi</strong> (Université Concordia), <strong>Andrea Schiffauerova </strong>(Université Concordia) et leur partenaire industriel <strong>Ashkan Ebadi </strong>(National Research Council Canada / Conseil national de recherches Canada).
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</p><h3>Résumé
</h3><p>Innovation is a dynamic process which involves a complex set of interacting and co-operating actors within the ecosystem. This webinar is going to focus on various unique actors which are critical for the ecosystem due to their impact on the knowledge and technology flows, on the collaborative behaviour and productivity of other actors and on the knowledge and innovation creation in the entire collaborative network. We use AI-related publications, patent information and various other sources, and employ an agent-based model using Python and machine learning algorithms. The study examines the effect of the critical actors by simulating various scenarios in the collaborative ecosystem. The research concludes by highlighting optimal patterns and features of the collaborative ecosystem within which critical actors maximize their positive effect on the ecosystem and by providing policy recommendations.</p>

<h3>À﻿ propos des présentatrices</h3><p><strong>
</strong></p><p><strong>Mahsa Noorinajafi   </strong>is  a PhD student in Information Systems Engineering at Concordia University. Her passion is working with data sets and implementing data analysis and machine learning algorithms. Her thesis focuses on the simulation of dynamic processes in complex environments. Currently she is creating an agent-based model to simulate critical actors behavior in a science-technology environment. </p><p>
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</p><p><strong></strong><a href="https://www.linkedin.com/in/andreaschiffauerova/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer"><strong>Andrea Schiffauerova</strong></a> is an associate professor at Concordia Institute for Information Systems Engineering. 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.</p>

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</p><h3>Partenaire</h3><p>
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</p><p><a href="https://www.linkedin.com/in/ashkanebadi/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Ashkan Ebadi</a> 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 out 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.</p>

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Critical actors in the collaborative ecosystem

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Critical actors in the collaborative ecosystem

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