̽��ֱ�� of Cambridge - Roberto Cipolla

Nine Cambridge scientists among the new 2022 Fellows announced by the Royal Society

jg533 — Tue, 10 May 2022 11:33:12 +0000

̽��ֱ��Royal Society is a self-governing Fellowship made up of the most eminent scientists, engineers and technologists from the UK and the Commonwealth. Its Foreign Members are drawn from the rest of the world.

̽��ֱ��Society’s fundamental purpose is to recognise, promote, and support excellence in science and to encourage the development and use of science for the benefit of humanity.

This year, a total of 51 Fellows, 10 Foreign Members, and one Honorary Fellow have been selected for their outstanding contributions to science.

Sir Adrian Smith, President of the Royal Society said: “It is an honour to welcome so many outstanding researchers from around the world into the Fellowship of the Royal Society.

“Through their careers so far, these researchers have helped further our understanding of human disease, biodiversity loss and the origins of the universe. I am also pleased to see so many new Fellows working in areas likely to have a transformative impact on our society over this century, from new materials and energy technologies to synthetic biology and artificial intelligence. I look forward to seeing what great things they will achieve in the years ahead.”

̽��ֱ��Cambridge Fellows are:

Professor Graham Burton FMedSci FRS

Mary Marshall and Arthur Walton Professor Emeritus of the Physiology of Reproduction, ̽��ֱ�� of Cambridge

Burton is a reproductive biologist whose research has focused on the early stages of human pregnancy. In particular, he showed how the placenta is established in a protective low-oxygen environment, stimulating its own development through interactions with the uterus. He demonstrated that aberrations in the early stages of placental development can adversely affect the life-long health of mother and offspring. Burton was founding Director of the Centre for Trophoblast Research, and founding Chair of the Strategic Research Initiative Cambridge Reproduction.

He said: “I am delighted to receive this recognition for myself and the field of reproductive biology, and thank colleagues and collaborators for their contributions over the years.”

Professor Roberto Cipolla FREng FRS

Professor of Information Engineering, Department of Engineering, ̽��ֱ�� of Cambridge

Cipolla is distinguished for his research in computer vision and his contributions to the reconstruction, registration and recognition of three-dimensional objects from images. These include novel algorithms for the recovery of accurate 3D shape, visual localisation and semantic segmentation and their translation into commercial products.

He said: "This is the ultimate honour for any scientist and recognises the amazing contribution of my students, collaborators and mentors in my 30 years at Cambridge. I am also very fortunate to be working in the field of computer vision and machine learning at a time of revolutionary progress and ground-breaking applications.”

Professor Douglas Easton FMedSci FRS

Professor of Genetic Epidemiology, Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, ̽��ֱ�� of Cambridge

Easton’s main research interests are in cancer genetics. He analyses large population studies to identify genetic variants that predispose to cancer, and to understand how they combine together with other factors to determine cancer risk. His work has characterised many important cancer genes such as BRCA1 and BRCA2, and identified of hundreds of common cancer predisposition variants in the non-coding genome. He co-developed the BOADICEA risk prediction model now used worldwide to guide genetic counselling and cancer prevention.

He said: "I am truly delighted and honoured to be elected to the Fellowship of the Royal Society. This prestigious honour is a tribute the work of many wonderful colleagues in Cambridge and worldwide, over many years, who have made the research possible."

Professor Robin Franklin FMedSci FRS

Formerly Professor of Stem Cell Medicine, Wellcome - MRC Cambridge Stem Cell Institute, ̽��ֱ�� of Cambridge; now Principal Investigator, Altos Labs - Cambridge Institute

̽��ֱ��central question of Franklin’s career is 'how do tissues regenerate?' To address this question, he has studied the brain, an organ notorious for its poor regenerative capacity. Working with many excellent colleagues, he has described how stem cells in the adult brain regenerate oligodendrocytes - the cells responsible for making the insulating myelin sheath around nerve fibres - once they are lost in diseases such as multiple sclerosis (MS); how this process declines with age; and it can be reversed. ̽��ֱ��work has led to two regenerative medicine trials in MS.

He said: “I am absolutely delighted to have been elected a Fellow of the Royal Society - it is a huge honour.”

Professor Richard Gilbertson FMedSci FRS

Li Ka Shing Chair of Oncology and Head of Department of Oncology, ̽��ֱ�� of Cambridge, Director of Cancer Research UK Cambridge Centre and Senior Group Leader, Cancer Research UK Cambridge Institute

Gilbertson, a paediatric physician-scientist, has identified the origins of common and aggressive childhood brain tumours and many of the genetic alterations that drive these tumours. His research has helped establish a direct link between disordered development and the multiple different brain tumour types observed in children: contributing directly to their classification by the World Health Organisation (WHO); changing the way conventional treatments are used, sparing children from unnecessary side effects; and underpinning clinical trials of new therapies.

Gilbertson said: “I am truly delighted and humbled to receive this recognition that I share with all the wonderful students, trainees and colleagues I have worked with over the years.”

Professor Paul Lehner FMedSci FRS

Professor of Immunology and Medicine, Cambridge Institute for Medical Research, ̽��ֱ�� of Cambridge

Lehner studies virus-host antagonism and how our genome is defended from invasion by RNA-derived retroelements such as HIV. His discovery of the ‘HUSH’ epigenetic silencing complex explains how the genome distinguishes new genetic material from endogenous genes through recognition of intronless DNA. This work uncovered an unanticipated surveillance system that discriminates ‘self’ from ‘non-self’ genomic DNA and defends our genome against the reverse flow of genetic information (RNA to DNA), paving the way to novel applications in medicine and biotechnology.

Lehner said: “I’m absolutely delighted to be elected to the Fellowship of the Royal Society; I’ve been fortunate to work with incredibly talented people and this honour recognises the commitment of the many past and present members of my group who have contributed to our work.”

Professor Roberto Maiolino FRS

Director of the Kavli Institute for Cosmology and Professor of Experimental Astrophysics, ̽��ֱ�� of Cambridge

Maiolino studies the formation of galaxies using observations collected at some of the largest ground-based and space telescopes. He has obtained key results on the interplay between the evolution of galaxies and the supermassive black holes at their centres. He has also investigated the enrichment of chemical elements across the cosmic epochs, as well as the origin and nature of dust particles in the early Universe.

He said: “I am truly honoured by such a prestigious appointment. Being a Fellow of the Royal Society will certainly foster my research activities and will allow me to further promote exciting, cutting-edge projects.”

Professor Angelos Michaelides FRS

1968 Professor of Chemistry, Yusuf Hamied Department of Chemistry, ̽��ֱ�� of Cambridge

Michaelides’ work involves the development and application of theoretical methods to better understand contemporary problems in chemistry, physics, and materials science. His group places a particular focus on developing and applying computer simulation approaches that provide the fundamental molecular-level insight needed to help address contemporary global challenges related to water, energy, and the environment.

He said: “Holy moly! I’m delighted to have been elected an FRS and very grateful to all the outstanding students, post-docs, collaborators, and mentors I’ve had over the years without whom this would never have happened.”

Professor Jason William Chin FMedSci FRS

Head, Centre for Chemical and Synthetic Biology, and Joint Head, Division of Protein and Nucleic Acid Chemistry, MRC Laboratory of Molecular Biology; Professor of Chemistry and Chemical Biology, Yusuf Hamied Department of Chemistry, ̽��ֱ�� of Cambridge; Associate Faculty in Synthetic Genomics, Wellcome Sanger Institute

Chin has engineered the genetic code of living cells to synthesise modified proteins and non-canonical polymers. To accomplish this, he created new translational machinery and codons to reprogram the genetic code, going well-beyond prior work using amber suppression. He then completely synthesised a bacterial genome in which he reduced the number of sense codons in its genetic code. ̽��ֱ��codons thus unused were reassigned to encode non-canonical amino acids. Chin's fundamental advances have been widely used to drive discovery, including to define the molecular consequences of post-translational modifications, define protein interactions in cells, and provide mechanistic insight into enzymes.

̽��ֱ��nine Cambridge researchers were all selected for their exceptional contributions to science.

It is an honour to welcome so many outstanding researchers from around the world into the Fellowship of the Royal Society.

Sir Adrian Smith, President of the Royal Society

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – as here, on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

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Computer vision app allows easier monitoring of diabetes

sc604 — Fri, 13 Nov 2020 11:50:28 +0000

̽��ֱ��app uses computer vision techniques to read and record the glucose levels, time and date displayed on a typical glucose test via the camera on a mobile phone. ̽��ֱ��technology, which doesn’t require an internet or Bluetooth connection, works for any type of glucose meter, in any orientation and in a variety of light levels. It also reduces waste by eliminating the need to replace high-quality non-Bluetooth meters, making it a cost-effective solution to the NHS.

Working with UK glucose testing company GlucoRx, the Cambridge researchers have developed the technology into a free mobile phone app, called GlucoRx Vision, which is now available on the Apple App Store and Google Play Store.

To use the app, users simply take a picture of their glucose meter and the results are automatically read and recorded, allowing much easier monitoring of blood glucose levels.

In addition to the glucose meters which people with diabetes use on a daily basis, many other types of digital meters are used in the medical and industrial sectors. However, many of these meters still do not have wireless connectivity, so connecting them to phone tracking apps often requires manual input.

“These meters work perfectly well, so we don’t want them sent to landfill just because they don’t have wireless connectivity,” said Dr James Charles from Cambridge’s Department of Engineering. “We wanted to find a way to retrofit them in an inexpensive and environmentally-friendly way using a mobile phone app.”

In addition to his interest in solving the challenge from an engineering point of view, Charles also had a personal interest in the problem. He has type 1 diabetes and needs to take as many as ten glucose readings per day. Each reading is then manually entered into a tracking app to help determine how much insulin he needs to regulate his blood glucose levels.

“From a purely selfish point of view, this was something I really wanted to develop,” he said.

“We wanted something that was efficient, quick and easy to use,” said Professor Roberto Cipolla, also from the Department of Engineering. “Diabetes can affect eyesight or even lead to blindness, so we needed the app to be easy to use for those with reduced vision.”

̽��ֱ��computer vision technology behind the GlucoRx app is made up of two steps. First, the screen of the glucose meter is detected. ̽��ֱ��researchers used a single training image and augmented it with random backgrounds, particularly backgrounds with people. This helps ensure the system is robust when the user’s face is reflected in the phone’s screen.

Second, a neural network called LeDigit detects each digit on the screen and reads it. ̽��ֱ��network is trained with computer-generated synthetic data, avoiding the need for labour-intensive labelling of data which is commonly needed to train a neural network.

“Since the font on these meters is digital, it’s easy to train the neural network to recognise lots of different inputs and synthesise the data,” said Charles. “This makes it highly efficient to run on a mobile phone.”

“It doesn’t matter which orientation the meter is in – we tested it in all types of orientations, viewpoints and light levels,” said Cipolla, who is also a Fellow of Jesus College. “ ̽��ֱ��app will vibrate when it’s read the information, so you get a clear signal when you’ve done it correctly. ̽��ֱ��system is accurate across a range of different types of meters, with read accuracies close to 100%”

In addition to blood glucose monitor, the researchers also tested their system on different types of digital meters, such as blood pressure monitors, kitchen and bathroom scales. ̽��ֱ��researchers also recently presented their results at the 31^st British Machine Vision Conference.

Gluco-Rx initially approached Cipolla’s team in 2018 to develop a cost-effective and environmentally-friendly solution to the problem of non-connected glucose meters, and once the technology had been shown to be sufficiently robust, the company worked with the Cambridge researchers to develop the app.

“We have been working in partnership with Cambridge ̽��ֱ�� on this unique solution, which will help change the management of diabetes for years to come,” said Chris Chapman, Chief Operating Officer of GlucoRx. “We will soon make this solution available to all of our more than 250,000 patients.”

As for Charles, who has been using the app to track his glucose levels, he said it “makes the whole process easier. I’ve now forgotten what it was like to enter the values in manually, but I do know I wouldn’t want to go back to it. There are a few areas in the system which could still be made even better, but all in all I’m very happy with the outcome.”

Reference:
James Charles, Stefano Bucciarelli and Roberto Cipolla. ‘Real-time screen reading: reducing domain shift for one-shot learning.’ Paper presented at the British Machine Vision Conference: https://bmvc2020-conference.com/conference/papers/paper_0512.html

A computer vision technology developed by ̽��ֱ�� of Cambridge engineers has now been developed into a free mobile phone app for regular monitoring of glucose levels in people with diabetes.

As someone with diabetes, this app makes the whole process easier. I’ve now forgotten what it was like to enter the values manually, but I do know I wouldn’t want to go back to it

James Charles

Reading diabetes monitor

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Low-cost AI heart monitor developed by Cambridge start-up

sc604 — Tue, 11 Sep 2018 14:00:23 +0000

̽��ֱ��company, Cambridge Heartwear, hopes to use its wireless monitor to improve the detection of irregular and dangerous heart rhythms and reduce the impact of stroke and stroke-related mortality and morbidity, which affects 120,000 people in the UK each year.

Professor Roberto Cipolla from Cambridge’s Department of Engineering met cardiologist and clinical academic Dr Rameen Shakur in 2015, a year after Roberto’s father had died of a stroke. Their ongoing research collaboration has now led to the formation of Cambridge Heartwear, a company based on the Cambridge Science Park.

̽��ֱ��company’s device, called Heartsense, includes a multiple lead ECG, oxygen sensing, temperature and tracking device which can be comfortably worn by patients for early screening. Sensors are enclosed in a robust waterproof casing, and the data produced is far more sensitive than that from current single lead wearable devices, as the development team have used their knowledge of clinical anatomy and electrophysiology to place leads for maximal signal output.

This data is wirelessly streamed in real time to the cloud where adaptive AI algorithms are able to identify clinically relevant irregular and dangerous rhythms just as a physician would. ̽��ֱ��device incorporates multiple independent sensors, in order to produce more specific and sensitive data than current heart monitors can provide.

̽��ֱ��research challenge was to produce algorithms that can learn from a limited amount of supervision from the cardiologist. “Our aim was not to replace the cardiologist, but to give them diagnostic support in real time,” said Cipolla.

To encourage the adoption by clinicians the team ensured that the output of the AI algorithms also included the information commonly used by a cardiologist. “This was not necessary for the final diagnosis but made the system a little more understandable and explainable than typical Deep Learning systems, which are still thought of as black boxes,” said Cipolla.

NHS figures suggest atrial fibrillation (AF), the most common heart rhythm disturbance encountered by doctors, affects in excess of one million people across the UK. According to national and international data, more than 80% of people who either die or are left with severe neurological deficits following a stroke had an irregular heartbeat as the underlying cause. However, irregular heartbeat is often diagnosed only after a person has had a stroke.

There are more than 100,000 strokes in the UK every year, and it is the fourth biggest killer in the in the UK, with more than 23,000 deaths last year. ̽��ֱ��NHS spends £2.5 billion annually on neurological treatment and rehabilitation for stroke patients.

“It makes sense to pick up AF before someone has a stroke and put preventative treatment in place,” said Shakur, who was formerly a Wellcome Trust Clinical Fellow at Cambridge and is now based at MIT. “Unfortunately, the technology and clinical care systems we currently have in place aren’t really doing this.”

Heart rhythms are currently measured by an electrocardiogram (ECG). To use an ECG as someone is going about their daily business, rather than in a GP surgery, a device called a Holter monitor is used. This requires fixing 12 leads to the patient’s chest and carrying the cumbersome device around for 24 hours.

It can take as long as four to six weeks from the time when a patient is referred by their GP to when the data from the Holter monitor is analysed and an irregular heartbeat is detected or not. Additionally, a Holter monitor costs as much as £2000.

“If you’re wearing an ECG over a long period of time, you’re collecting a huge amount of data,” said Shakur. “Finding an irregularity among all the normal rhythms can be like looking for a needle in a haystack. I wanted to automate this process, helping the patient to get a diagnosis and start on treatment.”

To solve this problem, Shakur began collaborating with Cipolla, a world leader in computer vision and real-world applications, and students from the Department of Engineering. ̽��ֱ��collaboration led to the founding of Cambridge Heartwear in 2017 and the development of the unique device and some powerful algorithms that can automatically interpret ECG data, which have an accuracy level in excess of 95%.

In 2017, the company secured funding to build and test 100 prototypes of the new heart monitor and to extend its AI capability. ̽��ֱ��Royal College of Art was also helped in the ergonomic design of the device. Heartsense will cost substantially less than a Holter monitor.

Clinical trials in Lancashire, UK have begun with patients enrolled from the primary care setting.

Inset image: (L-R) Roberto Cipolla, Rameen Shakur, James Charles

A Cambridge start-up has developed a low-cost next-generation wearable heart and cardiovascular function monitor which uses AI to diagnose heart rhythm and respiratory problems in real time.

Our aim was not to replace the cardiologist, but to give them diagnostic support in real time.

Roberto Cipolla

Cambridge Heartwear

Heartsense monitor

Yes

Teaching machines to see: new smartphone-based system could accelerate development of driverless cars

sc604 — Mon, 21 Dec 2015 06:34:09 +0000

Two newly-developed systems for driverless cars can identify a user’s location and orientation in places where GPS does not function, and identify the various components of a road scene in real time on a regular camera or smartphone, performing the same job as sensors costing tens of thousands of pounds.

̽��ֱ��separate but complementary systems have been designed by researchers from the ̽��ֱ�� of Cambridge and demonstrations are freely available online. Although the systems cannot currently control a driverless car, the ability to make a machine ‘see’ and accurately identify where it is and what it’s looking at is a vital part of developing autonomous vehicles and robotics.

̽��ֱ��first system, called SegNet, can take an image of a street scene it hasn’t seen before and classify it, sorting objects into 12 different categories – such as roads, street signs, pedestrians, buildings and cyclists – in real time. It can deal with light, shadow and night-time environments, and currently labels more than 90% of pixels correctly. Previous systems using expensive laser or radar based sensors have not been able to reach this level of accuracy while operating in real time.

Users can visit the SegNet website and upload an image or search for any city or town in the world, and the system will label all the components of the road scene. ̽��ֱ��system has been successfully tested on both city roads and motorways.

For the driverless cars currently in development, radar and base sensors are expensive – in fact, they often cost more than the car itself. In contrast with expensive sensors, which recognise objects through a mixture of radar and LIDAR (a remote sensing technology), SegNet learns by example – it was ‘trained’ by an industrious group of Cambridge undergraduate students, who manually labelled every pixel in each of 5000 images, with each image taking about 30 minutes to complete. Once the labelling was finished, the researchers then took two days to ‘train’ the system before it was put into action.

“It’s remarkably good at recognising things in an image, because it’s had so much practice,” said Alex Kendall, a PhD student in the Department of Engineering. “However, there are a million knobs that we can turn to fine-tune the system so that it keeps getting better.”

SegNet was primarily trained in highway and urban environments, so it still has some learning to do for rural, snowy or desert environments – although it has performed well in initial tests for these environments.

̽��ֱ��system is not yet at the point where it can be used to control a car or truck, but it could be used as a warning system, similar to the anti-collision technologies currently available on some passenger cars.

“Vision is our most powerful sense and driverless cars will also need to see,” said Professor Roberto Cipolla, who led the research. “But teaching a machine to see is far more difficult than it sounds.”

As children, we learn to recognise objects through example – if we’re shown a toy car several times, we learn to recognise both that specific car and other similar cars as the same type of object. But with a machine, it’s not as simple as showing it a single car and then having it be able to recognise all different types of cars. Machines today learn under supervision: sometimes through thousands of labelled examples.

There are three key technological questions that must be answered to design autonomous vehicles: where am I, what’s around me and what do I do next. SegNet addresses the second question, while a separate but complementary system answers the first by using images to determine both precise location and orientation.

̽��ֱ��localisation system designed by Kendall and Cipolla runs on a similar architecture to SegNet, and is able to localise a user and determine their orientation from a single colour image in a busy urban scene. ̽��ֱ��system is far more accurate than GPS and works in places where GPS does not, such as indoors, in tunnels, or in cities where a reliable GPS signal is not available.

It has been tested along a kilometre-long stretch of King’s Parade in central Cambridge, and it is able to determine both location and orientation within a few metres and a few degrees, which is far more accurate than GPS – a vital consideration for driverless cars. Users can try out the system for themselves here.

̽��ֱ��localisation system uses the geometry of a scene to learn its precise location, and is able to determine, for example, whether it is looking at the east or west side of a building, even if the two sides appear identical.

“Work in the field of artificial intelligence and robotics has really taken off in the past few years,” said Kendall. “But what’s cool about our group is that we’ve developed technology that uses deep learning to determine where you are and what’s around you – this is the first time this has been done using deep learning.”

“In the short term, we’re more likely to see this sort of system on a domestic robot – such as a robotic vacuum cleaner, for instance,” said Cipolla. “It will take time before drivers can fully trust an autonomous car, but the more effective and accurate we can make these technologies, the closer we are to the widespread adoption of driverless cars and other types of autonomous robotics.”

̽��ֱ��researchers are presenting details of the two technologies at the International Conference on Computer Vision in Santiago, Chile.

Two technologies which use deep learning techniques to help machines to see and recognise their location and surroundings could be used for the development of driverless cars and autonomous robotics – and can be used on a regular camera or smartphone.

Vision is our most powerful sense and driverless cars will also need to see, but teaching a machine to see is far more difficult than it sounds.

Roberto Cipolla

Teaching machines to see

Alex Kendall

SegNet demonstration

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. For image use please see separate credits above.

Yes

Caught on camera: engineering in action

amb206 — Mon, 15 Oct 2012 20:31:27 +0000

Engineering is about much more than dams and bridges. A glance at the hundreds of stunning images submitted for the 2012 Photography Competition at the Department of Engineering, sponsored by leading optical systems manufacturer Carl Zeiss, offers a window into a world of ground-breaking research in a staggering array of fields, encompassing applications that range from new adhesives and inkjet printing techniques through to the development of replacement human tissues.

Each year four prizes are awarded with the top photographers winning items of state-of-the-art equipment that will enable them to take their photography skills to new levels. Hundreds of images were submitted for the competition which asked for pictures that both related to research or teaching undertaken in the Department of Engineering, or out in the field, and were also interesting or beautiful as works of art in their own right. ̽��ֱ��engineering subjects captured in the photographs entered range in size from the tiniest atom-scale through to some of the world’s largest structures.

This year’s judges included Professor Roberto Cipolla, Dr Allan McRobie, Professor Dame Ann Dowling and Philip Guildford – all from the Department of Engineering. Also on the judging panel was Ken Robinson, Senior Applications Specialist at Carl Zeiss. ̽��ֱ��prize winners are announced today (15 October).

Philip Guildford, Director of Research at the Department of Engineering, explained that, given the high standard of entries, the selection process was ambitious with the panel looking for images that would hold their own in a gallery such as Tate Modern or Tate Britain. He also remarked that, while the competition had begun nine years ago as “a bit of fun”, it now played an extremely powerful role in communicating visually the sheer span of engineering and the excitement of working in a rapidly-developing discipline. “,” he said.

First prize for 2012 goes jointly to Dr Ronan Daly and Dr Alfonso Castrejon-Pita, of the Inkjet Research Centre, whose winning entry (Drying Patterns of AKD on Glass) shows the drying and cracking of a film formed when an alkyl ketene dimer (AKD) dispersion is deposited and dried on a glass microscope slide. ̽��ֱ��work of these researchers explores the new generation of inkjet printing techniques which have potential applications in diagnostic and lab-on-chip technologies that could speed up the process of identifying and treating life-threatening diseases.

Graham Treece wins second prize with his computer-generated image taken from a CT scan of the head. ̽��ֱ��image (A New Way of Looking) represents more than ten years of research into new techniques for measuring variations in the thickness of the cortex (the surface of the skull) and offers a valuable new tool for clinicians looking at injuries such as fractures. Third prize is won by Pola Goldberg Oppenheimer for her image (Nanoscale Fractal Branching Patterns) of adhesive structures that mimic the ability of the gecko’s feet to stick to surfaces repeatedly without losing their stickiness.

A separate monograph prize – for images captured using an electron microscope – is awarded jointly to Ching Theng Koh and Daniel Strange who are developing electro-spinning techniques that will produce networks of fibres with diameters one millionth of a metre or less. Their image (Electrospun Scaffold – A Fibrous Material with Nanoscale Fibres) shows the fibrous networks that mimic those found in many natural materials and have potential applications as human tissue replacements.

̽��ֱ��winning entries of the 2012 Photography Competition at the Department of Engineering, sponsored by Carl Zeiss, provide a stunning visual insight into the ways in which engineering makes a vital contribution to our lives.

Behind each image is a fascinating story of dedicated research.

Philip Guildford, Head of Research, Department of Engineering

Caught on camera: engineering in action

Dr Ronan Daly and Dr Alfonso Castrejon-Pita, Inkjet Research Centre,Department of Engineering

Drying Patterns of AKD on Glass

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Yes