Data Science and AI for Engineering and Physical Sciences
Engineering and the physical sciences have always been data-driven areas. However, with the rise of industry 4.0, combining data, physical and mathematical models has enabled much more realistic simulations and monitoring of complex systems.
Our expertise spans digital chemistry to social robotics and human-computer interaction, as well as significant world-leading research into energy control, forecasting and behaviour and environmental data analytics. Our researchers investigate AI for mobile and next generation communications, including privacy protection and facial recognition tools to safeguard our data.
Engineers work on a wide array of problems supported by AI including electronics design and application, computational mechanics, sensors and sensor networks, and critical infrastructure analysis. Our physicists use machine learning and AI to empower gravitational wave detection and other remotely-sensed solar physics phenomena, as well as next generation imaging techniques using quantum technology. Our expansive remit in this programme also includes investigating missing data, complex data fusion, the impact of climate change and spatial modelling of species distribution.
If you are interested in speaking to someone in regards to any of these activities, or related areas of interest, please contact our Programme Director directly, or alternatively get in touch via the Centre email address (cdsai@glasgow.ac.uk).
Programme Director: Dr Katy Tant
Senior Lecturer (Systems Power & Energy) and Honorary Lecturer (School of Engineering), James Watt School of Engineering
We asked Dr Tant to answer a few questions about her background with data science and AI and her hopes for the future of the Centre for Data Science & AI.
Can you tell us about your background in Data Science and AI, and how your experiences have shaped your approach to the programme you'll be directing?
My primary research interest lies in developing mathematical models and frameworks that enable us to work backwards from observed data to gain insights into the world around us. Specifically, I focus on interpreting scattered wave data to image the interior of solid objects and map their spatially varying material properties. This capability to look inside opaque objects has applications in a diverse array of fields, including non-destructive evaluation, medical imaging and diagnosis, and seismology. In recent years, my research group has expanded beyond traditional inverse problem frameworks to examine the use of deep learning to approximate the high-dimensional, nonlinear relationships between observed data and its causal factors.
My research sits at the interface between applied mathematics, data science, engineering and industry, and I am committed to promoting the need to work across these disciplines to tackle real-world challenges. I hope to use this collaborative, multi-disciplinary approach to inform and direct the Physical Sciences and Engineering Programme.
What are your key goals and aspirations for the programme you're leading, and what do you hope and/or envision the Centre’s impact on the wider University will be?
My programme aims to contribute to cross-university initiatives in three key areas: research, training, and resources. For research, I plan to facilitate cross-college networking to transfer knowledge across domains, building strong foundations and holistic approaches to tackle grand challenges set by research councils, government, and industry. In terms of training, I intend to evaluate the university's current Data Science and AI training materials, signposting these to researchers and staff, and collaborating with Centre staff to address any identified training gaps. Lastly, I am in discussions with IT and RCaaS to map out compute resources across the university, with the goal of enhancing transparency and accessibility to these services.
