Dr Katherine Tant

Strathclyde Chancellor's Fellow

Mathematics and Statistics


Personal statement

Dr Katherine Tant is an applied mathematician conducting research at the interface between mathematics and industry.  In June 2018, she was awarded a 3 year EPSRC UKRI Innovation fellowship for her work in ultrasonic tomography and was subsequently appointed as a Chancellor’s Fellow in the Department of Mathematics and Statistics at the University of Strathclyde in May 2019. Her research interests revolve around the modelling of waves in complex materials and using this knowledge to ‘see the unseen’; how can we create images of a solid object’s interior using wave data collected on its surface?  This is a hugely important question throughout many industries, including medical imaging, geophysics and non-destructive testing. Over the years, Katherine has worked closely with a number of companies including Doosan Babcock, EDF, IHI, NPL, NNL, Onscale and Rolls Royce. More information on the work conducted under her Waves, Inverse Problems and Imaging (WiPi) group can be found on the team's webpage.

As a result of her training and experience, Katherine is committed to promoting the role of mathematics in tackling real-world challenges and, in 2016, co-published the book UK Success Stories in Industrial Mathematics, which showcased mathematical research that had resulted in positive industrial, societal and environmental impacts. She is also an advocate for the advancement of women leaders in STEMM subjects. She is currently participating in the Homeward Bound programme, a global year-long leadership program for women in science which focusses on the development of leadership capacity, strategic capability and visibility and culminates in a 3 week intensive Antarctic voyage.


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Towards an in-process ultrasonic phased array inspection method for narrow-gap welds
Nicolson Ewan, Mohseni Ehsan, Lines David, Tant Katherine MM, Pierce Gareth, MacLeod Charles N
NDT and E International Vol 144 (2024)
Travel times and ray paths for acoustic and elastic waves in generally anisotropic media
Ludlam James, Tant Katherine, Dolean Victorita, Curtis Andrew
Journal of Computational Physics Vol 494 (2023)
Machine learning for real-time inversion of locally anisotropic weld properties using in-process ultrasonic array measurements
Pyle Richard, MacLeod Charles Norman, Tant Katherine Margaret Mary, Sweeney Nina, Ludlam James, Nicolson Ewan
60th Annual British Conference on NDT (2023)
Machine learning for real-time inversion of locally anisotropic weld properties using in-process layer by layer ultrasonic array measurements
Pyle Richard, MacLeod Charles Norman, Tant Katherine Margaret Mary, Sweeney Nina, Ludlam James, Nicolson Ewan, McKnight Shaun, Lines David
IEEE International Ultrasonics Symposium 2023 (2023)
Thermal compensation of ultrasonic transmit and receive data for steel welded plates at the point of manufacture
Foster Euan A, Sweeney Nina E, Nicolson Ewan, Singh Jonathan, Rizwan Muhammad K, Lines David, Pierce Gareth, Mohseni Ehsan, Gachagan Anthony, Tant Katherine MM, MacLeod Charles N
NDT and E International Vol 137 (2023)
Ultrasonic wave propagation in randomly layered heterogeneous media
Ferguson Alistair S, Mulholland Anthony J, Tant Katherine MM, Foondun Mohammud
Wave Motion Vol 120 (2023)

More publications

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Professional Activities

Traveltime Tomography of Locally Anisotropic Media Using SVGD
Mathematical theory and applications of multiple wave scattering
Traveltime Tomography Using Stein Variational Gradient Descent
Proceedings of the Royal Society A : Mathematical, Physical and Engineering Sciences (Journal)
Peer reviewer
Mechanical Systems and Signal Processing (Journal)
Peer reviewer
NDT and E International (Journal)
Peer reviewer

More professional activities


Improving Ultrasonic Imaging using Machine Learning - FIND CDT EngD with Rolls Royce
Tant, Katherine Margaret Mary (Principal Investigator) MacLeod, Charles Norman (Co-investigator)
01-Jan-2023 - 30-Jan-2027
Fusing a future from Glasgow’s proud heritage: Schedule Guaranteed High-Integrity Structures for a Secure, Safe and Resilient Transition to Net Zero (Innovation Accelerator)
MacLeod, Charles Norman (Principal Investigator) Dobie, Gordon (Co-investigator) Fitzpatrick, Stephen (Co-investigator) Gachagan, Anthony (Co-investigator) Javadi, Yashar (Co-investigator) Mohseni, Ehsan (Co-investigator) Pierce, Gareth (Co-investigator) Stratoudaki, Theodosia (Co-investigator) Tant, Katherine Margaret Mary (Co-investigator) Wathavana Vithanage, Randika Kosala (Co-investigator)
01-Jan-2023 - 31-Jan-2025
Remote ultrasound tomography using deep neural networks and laser ultrasonics
Tant, Katherine Margaret Mary (Co-investigator) Stratoudaki, Theodosia (Principal Investigator)
31-Jan-2021 - 20-Jan-2022
Doctoral Training Partnership 2020-2021 University of Strathclyde | Alfuwaires, Ahmed
Stratoudaki, Theodosia (Principal Investigator) Tant, Katherine Margaret Mary (Co-investigator) Alfuwaires, Ahmed (Research Co-investigator)
01-Jan-2021 - 01-Jan-2025
Ultrasonic Evaluation of Additively Manufactured Titanium Components
Tant, Katherine Margaret Mary (Principal Investigator) Blackburn, Dion (Post Grad Student) Wynne, Bradley (Co-investigator)
PhD studentship funded by the EPSRC CDT in Future UltraSonic Engineering,

Additive Manufacturing (AM) is a key driver of Industry 4.0, and has the potential to automate manufacturing of bespoke products with reduced costs, waste and energy consumption. This is of course highly desirable for companies not only in terms of profit, but in terms of sustainability and compliance with environmental targets and mandates. However, to unlock the full potential of AM in safety critical industries, it is imperative that the structural integrity of built parts is assured. Unfortunately, the lack of AM process stability, robustness and repeatability mean that it is difficult to control and predict the material properties of the built components due to the presence of defects induced by the equipment, process and/or feedstock inconsistencies. If this challenge cannot be overcome, the value of AM in high value industries is severely threatened.

To address this challenge, the development of reliable and robust in-process materials characterisation is vital. This would allow operators to adjust the AM control parameters online to compensate for any variations, or alternatively to discard poorly constructed components before they are completed, saving time, energy and money.

The central aim of this project is to develop models of ultrasonic wave propagation in the evolving microstructures of titanium alloys observed during metal additive manufacturing processes. We will examine ultrasonic propagation through both α-β phase (fig. 1a) and pure β phase (fig. 1b) structures to determine whether the larger scale β phase structures consistently dominate the effects on wave propagation (note that during the AM process, the alloy will cycle between these two states due to the applied thermal fields). These models will enhance our understanding of how ultrasound interacts with both the spatially and temporally changing microstructures, and will in turn facilitate the development of bespoke ultrasonic materials characterisation algorithms which will enable superior in-process imaging of the AM component. The student will study mathematical models of wave propagation through complex materials represented using statistical distributions, which will be informed by models of the microstructure evolution itself. These will be validated using commercial finite element modelling software. An inverse relationship between the transmitted wave forms and the descriptive statistics of the microstructure will be explored and used to monitor changes as the AM process progresses.
01-Jan-2021 - 01-Jan-2024
Maths DTP 2020 University of Strathclyde | Ludlam, James
Tant, Katherine Margaret Mary (Principal Investigator) Dolean Maini, Victorita (Co-investigator) Ludlam, James (Research Co-investigator)
01-Jan-2020 - 01-Jan-2024

More projects

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Dr Katherine Tant
Strathclyde Chancellor's Fellow
Mathematics and Statistics

Email: katy.tant@strath.ac.uk
Tel: 548 3647