Dr Katy Tant is a Senior Lecturer in AI for Engineering in the James Watt School of Engineering at the University of Glasgow. She obtained her BSc Hons in Mathematics from Heriot-Watt University in 2011 and a PhD in Mathematical Modelling for Ultrasonic Non-Destructive Testing from the University of Strathclyde in 2014.  In 2018 she was awarded a UKRI Innovation Fellowship to pursue her research in the field of ultrasonic tomography for non-destructive evaluation. She was subsequently appointed as a Chancellor's Fellow in the Department of Mathematics and Statistics at the University of Strathclyde in 2019 before moving as Senior Lecturer to the University of Glasgow in 2024.

Dr Tant works at the interface between applied mathematics, engineering and industry. Her central research interests include modelling waves in complex media, ultrasonics, inverse problems and imaging. As a result of her training and experience, she is committed to promoting the role of mathematics and data science in tackling real-world challenges and in 2016, she co-published the book UK Success Stories in Industrial Mathematics, which showcased mathematical research that had resulted in positive industrial, societal and environmental impacts. Dr Tant is also a passionate ambassador for women in science and diverse leadership. In June 2019, she was selected to participate in Homeward Bound, a global leadership initiative for women in STEMM. She currently leads Equity, Diversity and Inclusivity initiatives for PGR students which span the Universities of Glasgow and Strathclyde. 

In 2023, Dr Tant co-led a petition to establish an IEEE UK & Ireland Chapter in Ultrasonics, Ferroelcetrics and Frequency Control, which she now chairs. She is also co-lead of the UK Acoustics Network + Special Interest Group in Acoustic Non-Destructive Evaluation.

In today’s world, data driven inverse problems are ubiquitous. My central research objective is to develop mathematical models and frameworks which allow us to work backwards from observed data and derive insight about the world around us. I am particularly interested in interpreting scattered wave data to image the interior of solid objects and construct maps of 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. 

My research, and that of my group Waves, Inverse Problems and Imaging (WIPI), lies at the interface of applied mathematics, engineering and industry. Ongoing projects include:

• Deep Learning for Corrosion Monitoring in Pipelines (KTP with Novosound Ltd)
• Ultrasonic Characterisation of Micro Texture Regions (PhD Studentship)
• Variational Bayesian Inversion Approaches for Ultrasonic Tomography (PhD Studentship)
• Optimisation of ultrasound sensors for enhanced haptics (PhD Studentship with Ceramtec and Ultraleap)
• Nanobubbles for earlier detection of pancreatic cancer (PhD Studentship with CRUK)
• Deep Learning for Enhanced Ultrasonic Imaging (PhD Studentship with Rolls Royce)
• Physics Informed Machine Learning for Ocean Forecasting (PhD Studentship)

• Douglas, Leah
  Nanobubbles for Earlier Pancreatic Cancer Detection
• McKenna, Jason
  Optimisation of ultrasound sensors for enhanced haptics

Engineering Level 5: Advanced Artificial Intelligence and Machine Learning 5

Research fellowships

• 2018 - 2021: EPSRC Innovation Fellowship

Professional & learned societies

• 2021 - Current: Fellow, Higher Education Academy / Advance HE
• Member, IEEE (The Institute of Electrical and Electronics Engineers)
• 2024 - Current: Chair, IEEE UK & Ireland UFFC Chapter
• 2023 - Current: Member, Institute of Physics