Imaging, Data and Communications

Janet Forbes is a Prince2 qualified Project Manager. Since 2013 she has managed the University Defence Research Collaboration (UDRC); an academia led partnership between industry and defence. Funded by Dstl and EPSRC for the development of research in signal processing within the defence industry. Janet has promoted and developed the management of the communications strategy, technology transfer and reporting between academia, industry and defence. Janet’s project remit further comprises overall project coordination and includes managing the annual Sensor Signal Processing for Defence conference; organising UDRC themed meetings and implementing the annual UDRC Summer School for signal processing for defence. Other important accomplishments include an excellent record in technology transfer from the academic to application for the defence sector; development of effective working relationships and collaborations between industrial and academic partners; financial auditing and budget management and finally governs all non-technical aspects of the research programme.

Previously she managed a range of scientific projects within academia, industry and government and has an MSc in Marine Science from Heriot-Watt University (1997) and a BSc (hons) in Biological Sciences from the University of Plymouth (1992).

MSc in Marine Science from Heriot-Watt University (1997)

BSc (hons) in Biological Sciences from the University of Plymouth (1992)

Google Scholar

I am an Electrical Engineer interested in the fitting electromagnetic data to their respective models in the context of tomographic image reconstruction and model parameter estimation. I was educated in the UK and held research positions at School of Maths at the Univeristy of Manchester, the Lab for Information and Decision Systems at MIT and the Energy, Environment and Water Research Centre of the Cyprus Institute. In Edinburgh I lead the Agile Tomography Group that specialises in low-frequency electromagnetic simulation and tomographic image reconstruction, as well as chemical species tomography from spectroscopic measurements of light in the near infrared regime.

My research is in the realm of applied inverse problems and this usually entails, in some proportion: mathematical modelling, signal processing, statistical estimation and optimisation algorithms. As of 2016 I am also a faculty fellow at newly established Alan Turing Institute, which perhaps also qualifies me as a data scientist. My research is relevant to applications of electromagnetic imaging in geophysical exploration, industrial process tomography, biomedical imaging and non-destructive testing of materials and structures. In particular, I am interested in computational approaches suitable for large-scale pde models for static and low-frequency electromagnetic fields and algorithms that process these models along with measurements in the quest to image the electromagnetic properties of a domain of interest.

Nick Polydorides

(Office: 2.10 Alexander Graham Bell)

• Ph.D in Electrical tomography, in 2002 from UMIST (now The University of Manchester) with Bill Lionheart and Hugh McCann
• MSc in Computation, in 1999 from the University of Oxford
• BEng in Electrical Engineering and Electronics, in 1998 from UMIST (1st class Honors)

I am a member of IEEE Signal Processing and SIAM Imaging societies.

I am involved at the teaching of Engineering Mathematics 2A which involves mostly Laplace and Fourier series solutions of ordinary differential equations, which are essential elements of signal processing and understanding physical phenomena such as the propagation of sound, heat diffusion etc. I also take great pleasure from teaching the vector calculus and integration course in Engineering Mathematics 2B as it provides a good deal of insight to may of the physical phenomena us engineers need to understand. As for a teaching philosophy, I see myself standing in between the knowledge and the students, and although I cannot take the one to the other, I see my role as trying to make the distance look smaller.

Other than electromagnetics I have developed an interest in fluid-structure interaction models. This typically leads to a cluster of mechnical inverse problems relevant to the offshore energy exploration with towed arrays.

I am constantly looking to hire talented students who want to pursue a PhD because they want to make an impact to a certain application. If you have a numerate background and an appropriate course average from your BEng, BSc or MSc then I would like to hear from you at the email address above. Opportunities may also arise in the context of the Alan Turing Institute if your proposed theme falls within the so-called 'data-centric engineering'.

https://jacenkow.com

Grzegorz Jacenków is a PhD student at The University of Edinburgh. The core of his research is focused on integrating medical imaging and non-imaging data to improve decision support systems. His advisors are Prof. Sotirios A. Tsaftaris and Dr Alison O'Neil. His research is funded by EPSRC and Canon Medical Research Europe.

• BSc (Hons) Computer Science with Business and Management with Industrial Experience, The University of Manchester, 2017
• MSc Artificial Intelligence, The University of Edinburgh, 2018

• Teaching Assistant for Machine Learning for Signal Processing