Grant: Canada Research Chairs

Machine learning and Artificial Intelligence (AI) have a potential to impact all aspects of our lives and boast widespread economical and social benefits. The volume of connected devices such as smartphones, wearable devices, and environmental sensors, is growing at an unprecedented rate resulting in the explosion of data. While this is creating the foundation for smart systems such as smart cities, smart healthcare, smart grid, and Industry 4.0, the realization of these systems relies on our ability to analyze data. Machine learning is at the core of these smart systems because of its ability to learn from data, and provide data driven insights, process control, and decisions. But despite massive investments and tremendous successes of machine learning in recent years, a majority of collected data continue to be underutilized. Dr. Grolinger, Canada Research Chair in Engineering Applications of Machine Learning, is developing holistic and responsible machine learning solutions for smart communities, increasing the use of collected data, and delivering social benefits and economic opportunities. She envisions next generation systems that can be applied to a variety of engineering problems from smart cities to wearable devices in order to realize the potential of data and drive machine learning solutions in diverse engineering fields. Dr. Grolinger and her research team are developing distributed machine learning techniques suitable for large-scale smart systems such as those found in smart cities or Industry 4.0. Their approaches aim to provide holistic solutions that, in addition to accuracy, consider other factors such as computation cost, network traffic, latencies, and privacy. By developing dynamic and adaptable distributed systems, the team is ensuring that the smart systems continuously learn from and adapt to their environment. As Grolinger aims to break the barriers for wider use of machine learning in engineering, responsible AI remains a core consideration; the team is working on systems that can explain their decisions as well as ways to recognize and mitigate potential risks involved with machine learning. Ultimately, by pushing the boundaries of machine learning in engineering, Grolinger is creating new opportunities for the societal impact of machine learning. Working with industry partners, she has already established an innovative environment, and she will draw on this to support responsible adoption of machine learning.