Electronics and Electrical Engineering

Mr Danilo Bueno

dbueno@exseed.ed.ac.uk

1.02 Usher Building

Imaging, Data and Communications

Research Staff

Professor Michael E Davies

Director of Research

Mike.Davies@ed.ac.uk

+44(0)131 6505795

2.10 Alexander Graham Bell Building

Google Scholar

Electronics and Electrical Engineering

Imaging, Data and Communications

Accepting PhD Students

PhD projects

I am currently looking for PhD students under the following themes listed below. There is the opportunity to get university funding. As these are highly competitive, you are recommended to complete a formal application as soon as possible in order to be considered for any university funding opportunities.

Current Research Themes

Machine Imaging

Computational imaging relies on the acquisition of sensor measurements that indirectly inform about the imaged object and has a broad range of applications, from computational microscopy, medical imaging (CT, MRI, ultrasound), to sonar, radar, and seismic imaging. Current state-of-the-art methods are leveraging sophisticated machine learning (ML) solutions based on deep neural networks. However, supervised ML solutions necessitate unrealistic access to a large quantity of ground truth images.

One of the aims of this theme is to develop a foundational theoretical framework and algorithmic toolbox for learning to image with limited or no ground truth data. It will lay the foundations for a new wave of unsupervised ML-based computational imaging, with potential applications across a range of settings and imaging modalities from advanced medical imaging to robotics and autonomous systems. Unleashing the ML from ground truth data will enable the algorithms to exploit the larger quantities of unsupervised measurement data available to learn more complex and effective models leading to practical benefits of accelerated acquisitions and reduced imaging artifacts, as well as totally new imaging opportunities.

Data-Driven Computational Sensing and Imaging

Today's state-of-the-art imaging and sensing rely as much on computation as they do on sensor hardware. Furthermore, computational sensing and imaging is increasingly exploiting data-driven and machine learning solutions to enhance performance and develop novel hardware/software co-designed sensing systems. However, in critical scenarios such as medicine or defence and security it is vital that verifiable algorithmic solutions are used, which places restrictions on which machine learning approaches are admissible. Importantly, fully black box machine learning solutions should be avoided. This theme will therefore focus on the development of novel algorithmic and mathematical frameworks to exploit data and machine learning for imaging and sensing within a controlled explainable and verifiable manner. There will be a specific focus on RF and electro-optic/IR sensor modalities.

Sensor and Information Fusion

Sensor networks, sensor fusion and management techniques address key challenges in intelligence, surveillance, target acquisition, and reconnaissance (ISTAR). Opportunities in adaptive data-driven sensor tasking and resource management include adaptive sensor placement, adaptive waveform design to reflect the target reflection characteristics and channel environments, and adaptive sensor selection. Although these problems have solutions in specific use cases, this theme will consider scenarios with broader applications involving multiple heterogeneous sensors on single or multiple cooperative autonomous airborne platforms.

The solutions developed in this should be robust to dynamic and congested environments, adverse weather conditions, and mutual sensor interference. A range of algorithmic and signal processing or machine learning technologies will be considered, as well as specific technical challenges. For example, projects in this theme will consider aspects related to wide area motion imaging (WAMI), position, navigation, and timing issues (PNT); robustness to adversarial attack; sensor fusion and tracking applications; use of kernel and Monte Carlo methods; outlier-robust (and other metrics) messages in belief propagation algorithms; and scheduling in large dynamic networks. Probabilistic and Bayesian frameworks will be preferred to enable uncertainty quantification and management.

https://www.research.ed.ac.uk/portal/en/persons/michael-davies(130a49cb-c904-45…

Professor Ian Underwood

Director of International Partnerships

I.Underwood@ed.ac.uk

+44(0)131 6505631

3.11 Scottish Microelectronics Centre

Electronics and Electrical Engineering

Integrated Micro and Nano Systems

Ian Underwood carried out pioneering research and development in the field of liquid crystal microdisplays between 1983 and 1999.

His early career achievements were recognized by:

Fulbright Fellowship (1991) held at The University of Colorado
Photonics Spectra Circle of Excellence Award (1994) with Boulder Nonlinear Systems
British Telecom Summer Fellowship (1997)
Ben Sturgeon Award of the UK Chapter of the SID (1999)

In the late 1990's he contributed to the formation of Micropix Technologies (now Forth Dimension Displays) and consulted with them to design their first generation product line.

He is a co-founder of MicroEmissive Displays (MED) founded in 1998 and co-inventor of its P-OLED microdisplay technology. He served as Product Development Director, Director of Strategic Marketing then CTO.

MED's distinctions include:

2004 Guinness World Record for the world's smallest colour tv screen
European Semiconductor Start-up Award (2004)
the inaugural IEE Innovation in Engineering Award for Emerging Technology (2005)

MED raised in excess of 40M GBP Sterling and at its peak employed around 70 staff. MED went into administration in late 2008, an early victim of the global financial crisis, shortly after beginning to ship product for its first major order.

In 2005, while with MED, Ian was appointed in absentia to the Chair of Electronic Displays at The University of Edinburgh.

Recent personal recognition includes:

Ernst & Young Emerging Entrepreneur of the Year, Scotland (2003)
Fellow of the Royal Society of Edinburgh (2004)
Gannochy Medal for Innovation winner (2004)
Fellow of the Royal Academy of Engineering (2008)
Fellow of the Institute of Physics (2008)

In late 2007 he took up the Chair, becoming Head of the Institute for Integrated Micro and Nano Systems (IMNS) while remaining part-time at MED as Chief Scientific Adviser.

Until recently he sat on the Council of the Scottish Optoelectronics Association and the Steering Committee of ADRIA (Europe's Network in Advanced Displays).

He is known worldwide as an authority on microdisplay technology, systems and applications and is co-author of a recently released book entitled Introduction to Microdisplays (Wiley, 2006).

In late 2008 he was appointed to the Scottish Science Advisory Council the body that offers strategic advice on science and related matters to the Scottish Government.

Today he is employed by the University of Edinburgh for four days per week, seeking gainful employment on the fifth day as adviser, consultant, or non-executive director with pre-spin-out technology projects and early stage technology companies.

He is an Associate Editor of the Journal of the SID and sits on the technical program committee of the International Solid State Circuits Conference, the International Displays Research Conference and the Society for Information Display's Annual International Symposium.

In recent years he has made invited presentations and tutorials on microdisplays and related topics at major conferences and events throughout the world including:

Eurodisplay 2005 and 2009
IMID Korea 2006
SID USA 2007 and 2009
IEEE ISSCC USA 2009

Contact Ian by email at i.underwood@ed.ac.uk.

2008 Fellow of the Royal Academy of Engineering, FREng
2004 Fellow of the Royal Society of Edinburgh, FRSE

https://www.research.ed.ac.uk/portal/en/persons/ian-underwood(7e2fcca2-d579-484…

Academic staff

Dr Thomas Morstyn

Honorary Fellow

Thomas.Morstyn@ed.ac.uk

No Fixed Office

Google Scholar

ResearchGate

Electronics and Electrical Engineering

Energy Systems

I am an Honorary Fellow with the School of Engineering at the University of Edinburgh. My main position is Associate Professor in Power Systems with the Department of Engineering Science at the University of Oxford. I am also an Associate Editor of IEEE Transactions on Power Systems and Co-Chair of the IEEE Power & Energy Society Taskforce on Quantum Computing for Power System Operations. My research is focused on the design of control systems and markets to enable the large-scale integration of distributed power system flexibility.

University of Oxford web page: https://eng.ox.ac.uk/people/thomas-morstyn

PhD in Electrical Engineering, University of New South Wales, 2016.
BEng in Electrical Engineering with First Class (H1) Honours, University of Melbourne, 2011.

Senior Member, IEEE
Associate Editor, IEEE Transactions on Power Systems

PhD supervision.

Power system modelling and control
Electricity market design
Peer-to-peer energy trading
Electric vehicle smart charging and vehicle-to-grid
Microgrid control

If you are interested in collaborating or pursuing a PhD in one of my areas of research interest, please get in touch by email (thomas.morstyn@eng.ox.ac.uk).

Honorary Fellows, Visitors, Guests and Others