|November 23 – 24, 2018, Ho Chi Minh City, Vietnam|
Ishwar Puri is dean of engineering and professor at McMaster University in Hamilton, Ontario, Canada. He is a Fellow of the Canadian Academy of Engineering (CAE), and of AAAS and AIAA. His technical area of expertise is applied engineering science, translating fundamental research into applications and engineering practice. His contributions range from developing more efficient combustors, enhancing fire safety to designing thermal logic devices and developing magnetic inks for 3D printing. As an academic leader, he has initiated a digital learning and research strategy across engineering disciplines in the McMaster University Faculty of Engineering. He chairs the Canada-wide National Council of Deans of Engineering and Applied Science.
New disruptions – technological, business and social – are being facilitated by the evolution of digital technologies. There are, of course, many valuable pilot efforts across the world that integrate a deep understanding of new research on digital technologies with student learning. Yet the classical classroom that does not fully include digital learning dominates globally. There are many curricula that allow postsecondary students to learn about digital opportunities, but these are not ubiquitous and neither are they based on current advances. As educators, we want students to learn and become more self-aware about how their academic disciplines intersect with the enlarging digital world and new research. How do we do so? First, we must convey that digital transformation and innovation are not synonymous. While digital transformations are typically realized over longer periods, digital innovations on the other hand are generated by the more immediate ignition of creativity and relatively shorter term design thinking. Digital creativity is enabled both within the classroom and outside it, in the latter case sometimes even without a formal curriculum. It thus follows that, in addition to discipline-specific fundamentals, students and researchers should learn to embrace digital evolution regardless of their area of academic discipline to enable longer term digital transformation.
In this presentation, we will discuss some examples of how the awareness and understanding of digital evolution has been integrated into student learning and research in various engineering disciplines. This includes, for instance, teaching and learning related to new research in autonomous systems, data analytics, IoT implementations, e.g., for advanced manufacturing, and the evolution of graphical user interfaces and apps. Finally, we present some strategies to encourage the more ubiquitous implementation of these integrations.
University of Liège in Belgium
Dr. Ashwin Ittoo is a professor of Information Systems (Analytics/NLP/Machine Learning) at the University of Liège in Belgium. His main research area is in NLP, specifically, on minimally-supervised or unsupervised algorithms for semantic relation extraction. His research terms develop various machine learning and sophisticated econometrics methods, such including Deep Learning, Lasso and Ridge regressions, which are applied to diverse domains, such as finance and marketing.
Among his other activities, Prof. Ittoo is an Associate Editor (NLP, Machine Learning) of the Elsevier Journal, Computers in Industry, and has served as guest-editor for several special issues of the Elsevier Journal, Data and Knowledge Engineering. In addition, he has served/serves as Programme Committee Chair and Organization Chair of numerous conferences in the past.
He obtained his PhD in 2012 from the University of Groningen, The Netherlands, and his masters and bachelors degree from the Nanyang Technological University, Singapore and the National University of Singapore.Web site:
The topic of my talk is one that has only recently started to attract the interest of scientists and regulatory and governmental authorities, namely, that of Artificial Intelligence (AI) and Law. In particular, I will focus on two sub-domains of the law, which have been/will be most impacted by the emergence of increasingly sophisticated AI technologies.
The first sub-domain is that of competition law. I will describe a recent project in which we are investigating whether pricing agents, based on deep reinforcement learning can participate in tacit collusion, i.e. whether they can form cartels, just as humans would do. I will present various game-theoretic settings, which enable us to study the phenomenon of algorithmic tacit collusion.
The second domain is that of anti-discrimination law. In certain states of the US, algorithms are being deployed to predict the recidivism risk of defendants. These algorithms are trained to make predictions based on past data. However, studies by human-rights groups have shown that these data are inherently biased - they contain more instances of certain race/profile. Consequently, algorithms trained on these data, also suffer from the bias effect. The question here is how to remove bias during training?