Four Manchester academics made Fellows of the Royal Academy of Engineering

The Royal Academy of Engineering has today announced that four academics from The University of Manchester, Professors Danielle George, Alan Partridge, and Rich Curry, and Visiting Professor Kirsty Armer will be part of the newest cohort of leading figures in the field of technology and engineering to be appointed Fellows.

They will be joining the Academy in its 50th year and are part of a cohort of 75 new Fellows, who are making an incredible impact across the engineering and technology sector with innovations in clean energy, AI, medicine and public health; joining leading figures who have influenced academia and business, worked to widen participation in engineering for people from underrepresented backgrounds, and provided expert policy advice to government.

Professors George, Armer, Partridge, and Curry will be formally admitted to the Academy at a special ceremony in London on 19 October, when each Fellow will sign the roll book. In joining the Fellowship, they will lend their unique capabilities to achieving the Academy’s aim to engineer better lives.

Sir John Lazar CBE FREng, President of the Royal Academy of Engineering, said:

“This year’s cohort will be joining a community of over 1,700 top engineers and innovators in the UK and around the world. Together, we ensure that bright ideas are supported, scaled and shared, turning ambition into innovation and innovation into impact. We aim to strengthen education, skills and inclusion to inspire and equip the engineers of today and tomorrow.”

Manchester Academics awarded prestigious Colin Campbell Mitchell Award

The Academy has also awarded Professors Barry Lennox (Electrical and Electronic Engineering) and Michael Fisher (Computer Science) the prestigious Colin Campbell Mitchell Award, given to a team of engineers who have made an outstanding contribution to the advancement of any field of UK engineering across the past four years. Visiting Professor Kirsty Hewitson was also part of the team receiving the Academy honour.

This year, it is given in recognition of the pioneering approach that the University of Manchester has been taking in its approach to developing robotic systems for deployment in hazardous environments.

Their work has delivered significant engineering, safety and economic benefits, including robotic deployments that reduce human exposure to dangerous environments, estimated savings of around £20 million for Sellafield and the Nuclear Decommissioning Authority, and a projected future value of £500 million through wider adoption of the technologies developed. They have also pioneered the use of digital twins and simulation environments to improve safety, training and operational efficiency, while contributing to the development of robotics policy and responsible AI practices in the UK and internationally.

Chair of the Academy’s Awards Committee, Luke Logan (FREng), said of the research,

"By translating robotics and AI research into real-world industrial applications, this team has transformed how hazardous and complex engineering tasks can be carried out. Their work is reducing risks to people, improving productivity and delivering substantial economic benefits across multiple sectors. Through an outstanding collaboration spanning industry, academia and the public sector, they have demonstrated how engineering innovation can deliver real-world impact."

Multinex, a new model for low‑light image enhancement (LLIE), was created by Computer Science undergraduate Alexandru Brateanu during his third-year project, working with academic supervisors.

The model outperforms comparable compact systems, recovering detail and clarity from images that would previously have been considered unusable.

The advancement has significant implications for photography, security, and a wide range of computational imaging tasks.

Low‑light image enhancement seeks to restore natural visibility, colour fidelity, and structural detail in scenes captured under poor illumination. While recent LLIE models have achieved impressive results, many rely on heavy architectures with large parameter counts, resulting in high computational cost and limited real‑time applicability. Efficiency has therefore become a central research challenge: how to enhance images more effectively while dramatically reducing model size.

In the work presented at the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2026, the team proposes a structured solution grounded in classical colour vision theory and implemented using modern neural components within the Retinex framework. Retinex, a foundational approach in image enhancement, decomposes an image into illumination (light) and reflectance (colour) components to better handle low‑light scenes.

The design motivation behind Multinex is to extract as much useful information as possible from low‑light images using a highly compact architecture. By prioritising enhancement over reconstruction and leveraging lightweight neural operations, Multinex achieves strong illumination correction, detail recovery, and colour fidelity while using only a fraction of the parameters required by existing approaches.

The model is released in both a lightweight version (45K parameters) and an extremely compact nano version (0.7K parameters), each offering substantial reductions in computational load. Comparison to corresponding lightweight models such as PairLIE (330K parameters) and ZeroDCE (80K parameters) Multinex shows a significant performance improvement.

Like other LLIE techniques, Multinex still faces challenges in scenes with severe spectral distortions, lens flares, or mixed artificial and natural lighting. The team aims to extend the framework to these complex cases, exploring alternative formulations such as tone‑mapping or multiplicative residuals, and applying Multinex principles to related domains including intrinsic image decomposition, colour constancy, underwater enhancement, and haze removal.

The researchers demonstrate that Multinex delivers state‑of‑the‑art performance at real‑time cost, highlighting the power of combining analytic priors with modern lightweight design.

The prize of £1m will be awarded every year for the next ten years, to encourage AI research in the UK.

At 11am on 21 June, 1948 the Small Scale Experimental Machine (SSEM), nicked named ‘The Baby’, started running its first program. It took 52 minutes, running through 3.5 million calculations before it got to the correct answer.

In that process, the Baby became the first computer in the world to run a program electronically stored in its memory, rather than on paper tape or hardwired in.

Speaking in the House of Commons, the Chancellor said: “The world’s first stored program computer was built at The University of Manchester in 1948 and was known as the Manchester Baby. 75 years on the Baby has grown up, so I will call this new national AI award the Manchester Prize in its honour.”

Artificial intelligence research has gone from strength to strength at the University since then, building on the legacy of that achievement. Today the University works on fundamental AI, robotics and autonomous systems, advanced manufacturing systems and neuroscience.

To find out more about these exciting possibilities view our pages below.

Further information

Artificial Intelligence at the University

Robotics at the University of Manchester

Putting the Human back in to the Algorithm

How a 70-year-old ‘Baby’ changed the face of modern computing

Advanced materials and automation: manufacturing's 'dream team'

Radioactive robot Lyra named Best Invention of 2022

Digital Futures

Research Explorer

Through its partnership with HyperionDev, the DfE is offering over 1,400 potential learners the chance to enrol in a government-funded online coding boot camp.

These coding boot camps in Data Science, Software Engineering and Full-Stack Web Development, which can be completed within just 16 weeks, give learners a non-degree certificate from HyperionDev - with limited certifications issued in partnership with The University of Manchester and University of Nottingham Online.

Professor Danielle George, Associate Vice President Blended and Flexible Learning at The University of Manchester said: “Our new partnership with HyperionDev will make a significant contribution in addressing the national digital skills gap. Learning to code through boot camps aligns with our Flexible Learning Strategy and our commitment to prepare young people for an increasingly digital, interconnected and intercultural world.”

Riaz Moola, founder and CEO of HyperionDev says: "According to the UK government 2021 report “Quantifying the UK Data Skills Gap”, 48% of UK businesses were recruiting for roles requiring data skills. Of those, around 46% are struggling to find suitable candidates, meaning that there is a huge skills gap in this area. In the current economic crisis, the ability to code could significantly improve present and future employees' salary expectations. I strongly believe that accessible tech education is the future of upward social mobility for tens of thousands of people across the globe.” 

In a competitive job market, skills such as coding have become crucial both for young people and for experienced professionals looking to reorient their careers. According to Tech Nation’s “People and Skills” 2022 report, tech salaries in the UK are on average nearly 80% higher than non-tech salaries. This reality has increased demand for boot camps such as those designed and delivered by HyperionDev, which has expanded its operations significantly to keep up with the demand for its services. 

The HyperionDev programming boot camp courses are targeted at individuals who are looking to give their careers a boost, explore other career paths or to keep up-to-date with the latest job market requirements. They can generally be completed within three to six months and have been designed to help learners become fully-fledged developers, whether they are from a tech background or not. All students have a specifically designated mentor to help them adapt their level to the courses and advise them on their future professional development. 

The Network will be led by Professor Peter Haynes, Head of the Department of Materials at Imperial College London, and Professor Richard Curry, Vice-Dean for Research & Innovation in the Faculty of Science & Engineering at The University of Manchester. A strategic advisory board will be chaired by Professor Rachel Oliver from the University of Cambridge.

Professor Curry said “This Network will bridge the gap between the major investments in the NQTP and the Henry Royce Institute and help to secure the UK’s future competitiveness in quantum technologies."
 

Quantum mechanics is a fundamental theory of physics that was introduced to explain the behaviour of atoms and subatomic particles. It dominated the twentieth century by enabling the digital revolution that has transformed our economy and society.

We are now poised on the brink of a second revolution where the quantum physics of superposition and entanglement will be exploited at much larger scales. This will lead to transformative technologies for timing, sensing, imaging, communications and computing with applications in major industries including energy, construction, pharmaceuticals, defence, finance, security, telecommunications and information technology.

Like all technologies, quantum devices rely critically upon materials, both at the heart of the quantum system and in the surrounding technology. By comparison with the digital revolution, quantum technologies are currently at the stage of the thermionic valve: remarkable for their time but a long way from today’s products. While the materials of interest include so-called quantum materials such as superconductors and topological insulators, the majority of the needs at the heart of the quantum system will be met by more conventional complex oxides, ferroelectrics, nonlinear optical, 2D materials, engineered impurities in semiconductors, insulating materials, molecular materials, glasses and magnetic alloys, all underpinned by theory & simulation, characterisation and processing.

Professor Peter Haynes said “The EPSRC Materials for Quantum Network is a timely opportunity to harness the UK’s materials research community in addressing the needs of the national quantum programme to develop mature technologies that are sufficiently usable, reliable and cost-effective to take to market."

This certification is a significant accolade that gives students added confidence that they are going forward to deal with cyber security in a way that is "business as usual".

Professor Robert Stevens, Head of Department of Computer Science, said: “I’m delighted that our cyber security pathway has received this certification. Cyber security has always been important, but that importance is growing and I’m pleased that the Department of Computer Science’s contribution to the training of the next generation of cyber security experts has been validated in this way.”

In 2004, The University of Manchester worked with the National Computing Centre to enhance its teaching in computer-related security. This led to a computer security module that quickly became very popular. Over the years it has expanded to a pathway covering computer and network security and was joined by specialist teaching in cryptography and software security. The latter is taught by the world-class Systems and Software Security Research Group, famous for its award-winning testing tools.

This Computer Security pathway is now embedded in the MSc course and has seen over 2000 students through the programme. They get to tackle the challenges of cyber security that encourages them to go on to defend, innovate and grow the systems we rely on day to day. They learn from our academic team and practitioners in the field including Barclays, CISCO, Cyjax, KPMG, McAfee, NCC Group, North West Regional Organised Crime Unit, and Pentest Partners.

Each student engages in a significant research-based dissertation project that sees them tackling fundamental cyber security conundrums within the framework of the Cyber Security Body of Knowledge (CyBOK).

Chris Ensor, NCSC Deputy Director for Cyber Growth, said: “I am delighted that The University of Manchester's MSc in Advanced Computer Science (Security Pathway) is now fully certified by the NCSC. Offering a certified degree helps prospective students make more informed choices about their future career prospects in cyber security and employers can rest assured that graduates of these courses will be well-taught and have valued industry skills.”

Danny Dresner, Professor of Cyber Security at The University of Manchester, said: “Our policy has been to embed cyber security teaching with other opportunities including systems governance and machine learning so that our students can fulfil their learning objectives in computer science with cyber security being dealt with throughout the system's life cycle.”

Professor Dongarra is currently a Turing Fellow at The University of Manchester and collaborations with Manchester colleagues include work on batched computations, mixed precision arithmetic algorithms, and the PLASMA software.

As a leading ambassador of high-performance computing, Dongarra led the field in persuading hardware vendors to optimize these methods, and software developers to target his open-source libraries in their work. Ultimately, these efforts resulted in linear algebra-based software libraries achieving nearly universal adoption for high performance scientific and engineering computation on machines ranging from laptops to the world’s fastest supercomputers. These libraries were essential in the growth of the field—allowing progressively more powerful computers to solve computationally challenging problems.

“Today’s fastest supercomputers draw headlines in the media and excite public interest by performing mind-boggling feats of a quadrillion calculations in a second,” explains ACM President Gabriele Kotsis. “But beyond the understandable interest in new records being broken, high performance computing has been a major instrument of scientific discovery. HPC innovations have also spilled over into many different areas of computing and moved our entire field forward.

“Jack Dongarra played a central part in directing the successful trajectory of this field. His trailblazing work stretches back to 1979, and he remains one of the foremost and actively engaged leaders in the HPC community. His career certainly exemplifies the Turing Award’s recognition of ‘major contributions of lasting importance.’”

Professor Andrew Hazel, Head of Department, Mathematics at The University of Manchester said: "Jack Dongarra's pioneering work has made it possible for researchers around the world to access high-performance computing. The Department of Mathematics is delighted that his fundamental contributions have been recognised by the ACM Turing Award."

“Jack Dongarra's work has fundamentally changed and advanced scientific computing,” said Jeff Dean, Google Senior Fellow and SVP of Google Research and Google Health. “His deep and important work at the core of the world's most heavily used numerical libraries underlie every area of scientific computing, helping advance everything from drug discovery to weather forecasting, aerospace engineering and dozens more fields, and his deep focus on characterizing the performance of a wide range of computers has led to major advances in computer architectures that are well suited for numeric computations.”

Dongarra will be formally presented with the ACM A.M. Turing Award at the annual ACM Awards Banquet, which will be held this year on Saturday, June 11 at the Palace Hotel in San Francisco.

Dongarra has a 25% FTE appointment in the Department of Mathematics as Turing Fellow. He is a member of the Numerical Linear Algebra group and his work in Manchester has been funded by EPSRC and EU Horizon 2020 grants. He has also held Knowledge Transfer Partnerships with NAG Ltd., funded by Innovate UK.

The study, which compared 12 families of popular machine learning models to three standard statistical models for predicting a person’s risk of suffering a heart attack or stroke, is published in the British Medical Journal .

The researchers used heart attack and stroke as a case study, but argue most machine learning algorithms which estimate clinical risk are likely to encounter similar problems.

It is second time algorithms have been under fire in recent months: algorithms used by Ofqual to force-down A level results down similarly resulted in excessive lowering of grades.

And like the ill-fated A Level algorithms, the numbers generated by the models seem to be robust at a population level, but not at individual levels.

Recently, Machine Learning models have gained considerable popularity: the English NHS invested £250 million to further embed machine learning in health care.

Currently, GPs use a standard statistical tool (QRISK) to identify if their patients have a 10 per cent or greater 10-year risk of developing CVD. Those who do should be prescribed statins.

The study found that the predicted risks for the same patients were very different between Machine Learning models and QRISK, particularly for patients with higher risks.

Also, different Machine Learning models gave different predictions. Unlike QRISK, many Machine Learning algorithms are not able to take what statisticians call ‘censoring’ into account: patients move around, skewing the calculations downwards.

And of the 223,815 patients with a heart attack or stroke risk of greater than 7.5% with QRISK, 57.8% would be reclassified below 7.5% when using Machine Learning models.

“Patients will commonly drop out GP practices for a variety of reasons, but few Machine Learning algorithms build that into their modelling for large datasets,”, said co-author Professor Tjeerd Pieter van Staa.

“Even if a patient has been registered at a practice for a few months, the algorithms will treat it as 10 year’s-worth of data- resulting in a strong underestimation of clinical risk.

“And not only do they underestimate risk, there was a wide variance between the numbers, making it hard to see which model could be used by GPs for deciding treatments.”

He added: “Machine learning may be helpful in other areas of healthcare – such as imaging.

“But in terms of predicting risk we think a lot more work needs to be done before this technology can be used safely in the clinical setting.

“Perhaps the claims that Machine Learning will revolutionise healthcare are a little premature.”

The team tested the algorithms on 3.6 million patients from the Clinical Practice Research Datalink GOLD registered at 391 general practices in England from January 1998 to December 2018.