Our staff

 

Dr Ciprian D. Coman holds a PhD in Applied Mathematics from the University of Bath and started his current appointment in July 2019. Previous academic positions include the University of Exeter, Leicester, Glasgow, and the University of Nottingham. He has also held senior research positions at Schlumberger Ltd (Cambridge) and the National Physical Laboratory (Teddington/London).

In addition to his broad teaching experience at the above institutions, Dr Coman has also been involved in several nationwide teaching activities. He has lectured on ‘Continuum Models’ for the Scottish Mathematical Training Centre and has delivered similar material for the MAGIC consortium.

Dr Coman’s research interests fall within the area of theoretical and applied solid mechanics, with a special emphasis on mathematical modelling involving differential equations, singular perturbation techniques, elasticity theory, and the mechanics of thin plates and shells. While at Schlumberger (an oilfield services company) he worked on various aspects of drill string mechanics (e.g., steering, backwards whirl, PDC bits, rock fracture, etc), while in his role at NPL he provided mathematical support on a range of projects motivated by the electronics industry (e.g., IBM’s piezo-electronic transistors, etc) and biomedical applications (e.g., histotripsy). Dr Coman has a long-standing research interest in the asymptotic description of partial wrinkling instabilities associated with thin plates and elastic shells, an area to which he has contributed extensively for the past 15 years or so. He has held visiting research positions at several overseas institutions, which include the University of Western Australia, Trento University (Italy), Osaka University, and UC Berkeley.

Dr Coman currently teaches modules in Applied Mathematics and Mathematical Programming to first-year students on the Mathematics BSc(Hons).  As part of his involvement with the latter module, he has developed an online introduction to programming in Python (which covers both procedural and object-oriented approaches).

To view Dr Coman's Pure Profile and related publications click here

 

 

Professor Andrew Crampton has worked at the University of Huddersfield since obtaining his PhD there on the topic of approximation theory. Through his research he has collaborated with the scientists at the National Physical Laboratory, publishing research work and teaching short courses at both NPL in London and at Rolls Royce in Derby. He was a working group member for the Software Support for Metrology (SSfM) initiative.

Professor Crampton has worked with Thales Underwater Systems UK (TUSUK) developing algorithms for passive sonar detection and with Bentley Motors analysing brake disc squeal and judder. He is currently supervising an Innovate UK sponsored KTP associate, developing machine learning intelligence for supply chain modelling with an industrial partner - Valuechain.com.

More recently, he has been working with colleagues at Huddersfield developing deep learning models for defect detection in additive manufacturing, malaria detection and breast cancer screening. He currently teaches Computational Mathematics 2 (Linear Algebra) to final year Computer Science students and he co-ordinates the Master's Project in the Computer Science Department.

To view Professor Crampton's Pure Profile and related publications click here

 

Dr Alla Detinko joined the University of Huddersfield in 2021. Previously she worked at the University of St Andrews, after being awarded a Marie Curie Individual Fellowship from the European Commission under its Horizon 2020 programme. Her academic career also includes work at the University of Hull, and many years at the National University of Ireland, Galway.  

Dr Alla Detinko's research is in an innovative domain at the interface of algebra and computer science, dealing with the design of algorithms for practical computation with groups and related algebraic structures. This interdisciplinary research supports a host of applications throughout mathematics (algebra, topology, geometry, number theory), other sciences, and engineering. It is used to solve hard problems by group-theoretical modelling and computer-aided experimentation. Dr Detinko has been a key player in the establishment of practical computation with infinite groups acting on vector spaces, providing methods to handle the most important applications. 

To view Dr Alla's Pure Profile and related publications click here

 

 

Professor William Lee holds a PhD from the University of Cambridge and has worked at the Universities of Edinburgh, Hamburg, Portsmouth and Limerick. He currently holds an Adjunct Professorship in Industrial Mathematics at the University of Limerick. He has worked extensively with the industry on developing mathematical models to understand and improve industrial processes.

He has published research with industrial collaborators from Philips, Analog Devices, Rusal Aughinish Alumina, Teva Pharmaceuticals and DarkWoods Coffee. Collaboration mechanisms include Study Groups with Industry, consultancy, jointly funded research projects and bespoke training. Professor Lee’s research interests include:

• Industrial Mathematics
• Fluid mechanics
• Heat and mass transfer
• Professor Lee teaches Introduction to Mathematical Modelling and Problem Solving to first-year students on the BSc Mathematics Degree. His favourite research project involved modelling the fragmentation of volcanic rocks under explosive decompression.

To view Professor Lee's Pure Profile and related publications click here

 

 

 

Dr Ann Smith holds a PhD from the University of Huddersfield, an MSc in Applied Statistics (Sheffield Hallam University) and a BSc in Mathematics (University of Hull). In addition, she holds a PGCE(FE) and is a Fellow of both the Institute of Mathematics and its Applications and the Higher Education Academy.

As a member of the CEPE research group Dr Smith provides statistical expertise to support the group's work on condition monitoring for predictive maintenance. Her research interests include an input parameter volume reduction, non-linear systems approach to detection of deviant events, autonomous abnormality assessment, evidence-based healthcare diagnostics, mathematics education and e-learning.

Dr Smith is a visiting lecturer at Fuzhou Normal University, China and also worked at Universitaet Greisfault, Neubrandenburg, Germany. Additionally, she worked in collaboration with Calderdale and Kirklees Health Authority as a statistical consultant to promote evidence-based health care building on her MSc thesis on near-patient testing of blood glucose measurements in primary care which informed practice protocol. 

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Dr Sofya Titarenko holds a PhD in Applied Mathematics (Department of Physics) from Moscow State University. She started her career developing and implementing fast algorithms for removing ring artefacts from computerized tomography images (CT scans). The algorithms are based on ill-posed inverse theory and Tikhonov regularization. The codes developed have been integrated into the Daresbury Light source synchrotron facility.

After that, she worked in computational geophysics at the University of Leeds, building numerical models for oceanic hydrothermal circulation and for seismic waves propagating through fractured media. She further developed her skills and interest in high-performance computing, making the seismic code work quickly and efficiently.

More recently she moved into the Data Science area with a position in the Leeds Institute for Data Analytics (LIDA), working on fast algorithms for pattern mining. This work has continued at the University of Huddersfield with the application of Machine Learning tools to industrial and medical problems.

Topics of interest include:

·         Fast algorithms in Data Science and applied mathematics

·         Application of Machine Learning algorithms to various fields, including medicine, bioinformatics, engineering, social sciences.

·         Computer vision

·         High-Performance Computing and Optimization

Current projects:

·         Machine Learning techniques applied to identify significant gene biomarkers for efficient cancer treatment

·         Machine Learning to Guide Malaria Diagnostics and Treatment (in collaboration with London School of Hygiene and Tropical Medicine)

·         Deep Learning to improve breast cancer detection

·         Data Analytics and Machine Learning to Help Understand Student Performance

·         Machine Learning applied to Alzheimer dataset (title need to be confirmed)

·         Photovoltaic Fault Detection (title need to be confirmed)

To view Dr Titarenko's Pure Profile and related publications click here