Engineering & Computer Science

Professor Rob Mahony

‘I started out with a science degree in pure mathematics and geology. I guess I was the only student in the university with that particular combination at the time. I did maths out of love for the subject, but I always wanted to be involved in something more applied, and geophysics and structural geology seemed a good option. At the time the ANU did not have engineering or applied maths, so in a way I made my own applied degree — ANU has always been good at providing students with flexible study options’, says Robert Mahony.

‘After my degree I worked for a geophysics company in Canberra in the area of marine seismic exploration, but I soon realised I really wanted the challenge of a job with a research focus and I chose to go back to study a PhD. I went looking for a research group that was doing world-class applied research — I thought that if they found it interesting and exciting then so would I. I was lucky enough to end up in systems engineering working with Professor John Moore.’

Robert received a PhD in 1995 in systems engineering at ANU before completing a postdoctoral fellowship in France and a Logan Fellowship at Monash University. He returned to Canberra and the ANU in 2001, becoming a Professor in the Research School of Engineering in 2011.

‘My work is in using sensor systems to control robotic systems. I started work on motion control of vehicles during my PhD, and then in France began work on aerial vehicles’, says Robert.

Robert explains that aerial vehicles that deliver images to people on the ground are a growth industry as technology advances. ‘For example, 15 years ago the inertial measurement unit that is a key component of such a vehicle cost $3000 and weighed 500 grams. Now it costs $30 and weighs 50 grams. As the vehicles improve and become more affordable the uses are expanding. There are thousands of start-up companies world wide offering aerial vehicles for a wide range of tasks.’

‘As well as being a boon to the film and television industry, the vehicles can be used for all sorts of inspections — for example you need to regularly inspect dam walls and bridges for cracks or corrosion, normally a labour-intensive process done by abseiling. In industry there are many parts of a plant that need inspection, from large pressure vessels to cooling towers and pipes on factory ceilings. On the smaller scale, electricians or phone technicians can use aerial vehicles to inspect problems with poles or building connections.’

‘In safety and security, firefighters already use such vehicles to look through the windows or a burning building to see if there are flames or people. Police can use them to survey crowds and demonstrations, and protesters also use them to video and document police practice.’

‘A fundamental challenge in aerial vehicles,’ explains Robert, ‘is in sensing the vehicle state, a mathematical description of the position, velocity and orientation of the vehicle relative to its environment.’ He developed an attitude ‘state observer’ that has become the industry standard for quadrotor vehicles, and is now working on exteroreceptive sensing paradigms — sensor modalities that provide information relative to the surrounding environment. Robert believes that vision systems are a key technology in this area. ‘Such systems are inexpensive, light, require low power, and are rich in information. However, using vision to control aerial vehicles is complex.’ He has made key contributions in the formulation and analysis of image-based servo control for aerial robots.

A further constraining factor in current technology is that you need to be an expert pilot to fly an aerial vehicle. He is developing technology for pilot-assist systems which will make it possible for anyone to fly a quadrotor with no training.

When not working on new technology, Robert is also a musician in a several folk bands, a cyclist and sailor.

Dr Antonio Tricoli is a Research Fellow in the College of Engineering and Computer Science at The Australian National University.

Nanotechnology is a multi-disciplinary field where the most heterogeneous combination of scientists meet, steadily providing new inputs for ground-breaking discoveries.

‘There is indeed plenty of room at the bottom (also for new ideas) and Nanotechnology is the key to access it‘, says Antonio

Nanostructures - broadly defined as materials in the range of 1 to 100 nanometres — can allow direct manipulation of matter on an atomic and molecular level in a scalable and accessible way that is of practical relevance to everyday needs

Antonio received his Master in Mechanical and Process Engineering from the Swiss Federal Institute of Technology (ETH Zurich) in 2004. He continued his studies at the Particle Technology Laboratory and was awarded the HILTI prize in 2009 for the most innovative PhD thesis in ETH Zurich. Antonio then became a research fellow and lecturer at the Mechanical and Process Engineering Department of ETH Zurich and in 2012 he joined ANU under the Future Engineering Research Leadership Fellowship program. He is currently investigating the self-assembly of tailored, smart nanostructures for application in biotechnology, water treatment, solid-state technology, energy production and storage.

‘The population of the world is growing and ageing, and our current use of natural and human resources is not sustainable. In the coming decades, preserving our quality of life will require drastic changes in key technologies that are in many ways comparable to the industrial revolution. As the steam engine became the driver for an incredible number of technological possibilities, today smart nanomachines may become a catalyst able to trigger a revolution in our approach to material synthesis, energy harvesting and overall interaction with our surroundings.’

Nanotechnology is already being used in many areas. Medical applications of nanotechnology are entering a commercial stage, ‘we are investigating non-invasive methods to detect and monitor illnesses that have the potential to drastically reduce the cost of medical diagnoses and make extremely early-stage diagnosis possible. For example, several gases present in the breath when you have a disease have been successfully identified, and we have recently demonstrated that low-cost nanosensors can analyse the breath with great accuracy. They can detect breath-acetone which can be used to diagnose diabetes. In a similar way, early-stage diagnosis of some cancer types may be possible and will greatly improve the chances of patient recovery.’

‘We are also looking at ways to improve diabetic monitoring. In a not too far future, instead of having to measure blood sugar levels by taking blood samples, someone with diabetes could wear a wristband with a nanotechnology chip in it that would measure the levels directly from their skin.’

‘As the world is facing some major changes, nanotechnology may not be just an ‘option’. However, to ensure a successful and risk-conscious approach to the synthesis and handling of such powerful machines, a comprehensive understanding of the fundamental forces controlling their interaction with the environment is a must — something which my research group strives to achieve.’

Antonio is looking forward to contributing to the development of nanotechnology research in Australia and around the world. ‘When I was in Zurich and choosing where to go to continue my career, Australia seemed to have the best balance of opportunities. Australia is recognising the need to invest in research both for the economy and for future technology needs’

Outside work, moving from Switzerland to ANU has also brought Antonio a bit closer to his second passion — ocean sailing. ‘In the weekends, I would drive six hours through the Alps to reach the Mediterranean coast and spend some time in the waves.’.

Bob Williamson is Professor in the Research School of Computer Science. His area of expertise is in the field of machine learning.

"Machine learning is about recognising patterns in data. Anything can be conceived and recognised as a pattern. So for example we can recognise voices or handwriting, or identify a contaminant in the environment."

Bob's interest in electronics and computing started early. "When I was eight my dad gave me an electronics set. Then in high school I was into ham radio - talking to people all over the world. That got me into signal processing which eventually led to my interest in machine learning - signal processing is about extracting signal from noise and machine learning does this with more complicated 'signals'."

Bob completed his undergraduate degree in electrical engineering and conducted his PhD research into reasoning with uncertainty, but he is a little different to most machine learning researchers, who usually work on one individual data problem. By contrast, Bob wants to understand the whole of machine learning.

"Machine learning is becoming increasingly important because we have increasing amounts of data to deal with. The problem at the moment is that we don't have the basic tools to make sense of it in a manner that is easy to use," explains Bob.

"For example, if you are a mechanical engineer building an engine then you don't have to start by inventing the nuts and bolts - that work has already been done, and you have standard bolts to work with. But in machine learning, while the nuts and bolts exist, they are not standardised. And consequently they are continually reinvented. I would like to encourage this standardisation process by first understanding the problems and the relationship between them."

This is Bob's current research agenda. But what of the past? Two of his key contributions are an algorithm and a theory.

"The algorithm - the one class support vector machine - allows detection of what is 'unusual' in a set of data by simply showing lots of 'usual' examples. For example, if you have an engine, you might want to be able to detect when it is not running normally. To do this we would usually need to define exactly what we meant by 'not normal'. Instead, this algorithm allows the computer to learn for itself what is usual from a set of data, and then to be able to spot the unusual. This algorithm is now widely used as it allows researchers from any field to be able to recognise the unusual in their data," says Bob.

"The theory is 'luckiness'. When you are doing research you need to know before starting how much data you will need to be able to draw accurate conclusions. For example, do you need to collect 20 or 20000 samples to be able to test your handwriting recognition software? There are theories that can give you this information, but sometimes they are very pessimistic. Sometimes the world is nice to you, and you will be able to draw accurate conclusions with less data than you expect. 'Luckiness' is a rigorous mathematical theory that explains when this can occur."

Bob hopes that his research will help to turn machine learning into an engineering discipline. "If this is done, then machine learning will be able to contribute enormously to almost every area of human endeavour."

Stephen Gould is Fellow in the Research School of Computer Science at The Australian National University.

"I was always interested in engineering and computers as a kid - I was devoted to Lego and got my first computer when I was 13. I spent all my time on that Apple IIe and I still have it, though now I tend to spend all my time on the iMac it sits next to."

Stephen received a PhD in Electrical Engineering and a Master of Science in Electrical Engineering from Stanford University, having completed Bachelor of Engineering and Bachelor of Science degrees at the University of Sydney.

"I now work at the intersection of two areas of computer science, computer vision and machine learning," says Stephen. "In computer vision, the goal is to get the machine to understand its visual input, so that it can detect and identify objects. In machine learning, the computer can learn from situations and improve its performance.

"Bringing the two together means that the computer can learn from the scenes it is shown and improve its recognition of particular objects. I want to be able to give a machine an image and for it to come back with a comprehensive description of what it is 'seeing'".

Computer vision and machine learning have many applications.

"Currently, when you search for an image in Google, what the program is really searching for are the words around the images. With effective image recognition you could get the computer to search for images directly, either from the words you give it or from a sample picture.

"There is a lot of effort being put into cars that drive automatically, without any driver. That means the car's computer needs to be able to see and recognise many things - roads, pedestrians and other hazards, road signs and so on.

"There are even applications in biomedical imaging. State-of-the-art medical imaging technology, such as MRIs, often use image recognition software for diagnostic systems to aid doctors. We are using image recognition to monitor the development of cells in an embryo, which will give us a greater understanding of cell development and may also help in developing new IVF techniques."

Stephen's career has spanned both academia and industry. Between completion of his Masters degree and starting his PhD research, Stephen worked in industry for a number of years. He co-founded Sensory Networks, a network security start-up company, and has developed freely available software libraries for machine learning and scene understanding. He holds eight international patents.

"My most notable achievements have been the start-up company that I co-founded, and just the satisfaction of the contributions I have made to the machine learning of the future," says Stephen. "I would like to be involved in other start-ups and new products - the whole area of moving between ideas and development to an actual product is a fascinating one. I would also like to mentor students in this as I think they benefit greatly from this kind of interaction."

"I have two kids and one more on the way and they keep me busy. I have also taken up woodworking, which is a great gadget hobby for an engineer, and it does get me away from the computer. In woodworking my most notable achievement is the toy kitchen I built for my kids!"

Brendan McKay is Professor in the Research School of Computer Science at The Australian National University.

"I was always interested in science, and in school I wandered around physics and chemistry. But I kept coming back to my first love, mathematics."

Brendan received his PhD from Melbourne University in 1980. After three years at Vanderbilt University in the United States, he came to ANU in 1983. He has been Professor of Computer Science since 1998.

"I am a theorist. I look at the fundamentals of mathematics in relation to networks," says Brendan.

"A network is basically a collection of things with a relationship. All sorts of things can be a network - for example a set of cities is a network, and their relationship is the roads between them. Networks appear in every research area and walk of life, from psychology to engineering.

"My main focus is in 'random' networks; in other words, networks that 'just grow'. The Internet is a perfect example of a random network. Some parts have been planned, but in general it has developed through the independent and random decisions of many people.

"By developing a mathematical model of random networks we can use it to predict their future. In the case of the Internet, we can see how it might grow and thus what might be needed in future technology and infrastructure. We can also look at what might threaten it - how secure it is and how many nodes would need to be destroyed for it to be damaged."

Brendan is best known for his work on graph isomorphism - seeing whether two networks are really the same. The software he developed for this in the 1970s has been regarded as the leader in the field, and he is now updating it in collaboration with other researchers.

Brendan's work has a wide range of applications. "Since I am a theorist my work is generally used by others. But being theoretical does not mean that it is not practical," he says.

"In random networks, almost any field can use the theories and algorithms to see how a network develops - which can be used in the study of social processes or the spread of diseases. In graph isomorphism, my work has contributed to an amazing number and variety of other research projects, in particle physics, computer architecture, artificial intelligence, cryptology and many others. I am surprised and delighted when someone thinks of another application for my work."

Another area of study for Brendan is 'structure enumeration', finding all the possible networks that satisfy a particular rule. The most obvious application of this is in chemistry. Chemical molecules can be arranged in various ways, known as isomers. As molecules get larger, so do the number of ways the atoms can join up. Finding the potential atom 'networks' on a computer allows chemists to try to create new substances with new properties in the laboratory.

Brendan is 60 years old, however he has no plans to stop researching. "I have so many projects - seven or eight at any time. I expect I will go very slowly into retirement."

Brendan's other main interest is in debunking pseudoscience. In 2004 he received the Australian Skeptics Eureka Prize for Critical Thinking for his work in spreading the truth about the 'Bible codes'. "The fight against scientific ignorance in the community, and especially against those who foster it deliberately, is a worthwhile use of my professional talents."

David Nisbet is Research Fellow in the Research School of Engineering at The Australian National University.

"I am developing new materials that support the growth of human cells. More specifically, I engineer synthetic environments for stem cells that promote their survival and function. From this we hope to regenerate brain cells and pathways that have become damaged, either through injury or through diseases such as Parkinson's disease."

David Nisbet works in the area of biomedical engineering at the ANU College of Engineering and Computer Science, and his work has some important and exciting applications.

David says, "We are engineering new materials that mimic the morphological and chemical features of brain tissue. This provides an environment where stem cells can grow and function.

"With this new material we have been able to control inflammation - which is one of the main reasons that cells do not regenerate after injury - and encourage nerve regeneration. We have been able to implant a synthetic material into the brain and have cells migrate into the artificial structure.

"Successful stem cell transplantation technology would be the means of replacing lost brain cells. It would represent a major scientific breakthrough and revolutionise medical treatments."

However, biomedical engineering was not David's first choice of career.

"I originally wanted to be a carpenter and then started a civil engineering degree at Monash University. After my first year I became interested in the idea of making novel materials, and so moved into materials engineering."

After obtaining a PhD in Materials Engineering in 2009, David received an Australian Postdoctoral Fellowship to pursue research in tissue engineering and the fabrication of artificial stem cell microenvironments. In 2010, he was awarded the prestigious Fulbright Scholarship to spend six months studying surface science and biofunctionalisation at the University of California Berkeley. He is a chief investigator on two Australian Research Council discovery projects and two National Health and Medicine Research Council project grants.

"One of the most important features of my work is 'translational medicine'. I am not just interested in making new materials, but in making sure that those materials can be translated into the clinic and be used by doctors and surgeons to improve someone's health."

For this reason, the emphasis of David's work is at the interface of several disciplines.

"Our team brings together mechanical engineering, chemistry, biology and fabrication skills and knowledge. We need all of these to give our new materials the structural, mechanical, chemical and biological properties that will support human cells. I think biomedical engineering is an area for growth in both medicine and in engineering, and I hope to be able to grow the group at the ANU and create new linkages with medical researchers both locally and internationally."

Moving to ANU from Melbourne has also meant that David has the chance to spend more time at one of his favourite pastimes - cycling. "Mountain biking in Melbourne means an hour's drive out of the city - here in Canberra I can do it on the way home from work!"

Professor Andrew Blakers

"I studied physics and maths at school and at ANU, intending to become an astronomer, but then decided that solar energy was a much more useful area of study because I had become interested in conservation. I completed my PhD at the University of New South Wales working under the supervision of Martin Green," says Andrew.

"I work in several areas of solar energy.

"One of the new technologies we have been developing at ANU are SLIVER solar cells. These efficient cells use much less silicon than conventional cells. A conventional solar panel needs about 60 silicon wafers to convert sunlight to 140 watts of power - in comparison the SLIVER cells need only the equivalent of two silicon wafers. Silicon is the costliest part of solar panels today and the SLIVER cells dramatically reduce the amount needed.

"Also, SLIVER cells can be lightweight, flexible and transparent. They could make many new applications possible, such as flexible SLIVER panels for building roofs and roll-up portable solar panels."

One of Andrew's other research areas is microconcentrators. In concentrator photovoltaic systems, sunlight is focused on the photovoltaic cells, using optics such as mirrors and lenses, to increase the amount of sunlight the cells receive. In partnership with Chromasun Inc, ANU is developing a roof-mounted microconcentrator system that can concentrate sunlight up to 30 times and deliver both electricity and thermal energy to buildings.

Andrew's work has been recognised with a number of awards, including the Engineering Excellence Award for SLIVER technology from Engineers Australia; the Weeks Award for Achievement Through Action from the International Solar Energy Society; and the ACT Sustainable Cities Environmental Innovation Award in 2006.

The ANU solar energy group is one of largest in Australia, with about 80 staff and PhD students working in photovoltaics, solar concentrators, thermal and hybrid photovoltaic–thermal systems.

"This group is one of my main achievements," says Andrew.

"I founded the photovoltaics group 20 years ago, building upon the pioneering work of Stephen Kaneff and colleagues in solar thermal concentrators from the early 1970s. It has grown in breadth and depth of skills, disciplines and experience. As well as the technologies we hope to develop, we are also developing people. These are the key ingredients that are needed for significant progress to happen.

"I aim to further grow the group - without critical mass we can't take our ideas to the next level of development.

"I also get involved in the policy and economics of solar energy and sustainability. This is an area not just about the science but also about how implementation can be supported and improved, so that meeting our energy needs in Australia and globally becomes sustainable in the long term."

Kylie Catchpole is Research Fellow at the ANU Research School of Engineering. She describes the aim of her research very simply, as "better, cheaper solar power".

"My research is basically about putting tiny particles of silver on the top of solar cells to make the cells work better. Even the most efficient solar cells today reflect some light, so energy is lost. These particles act as antennas to transmit the light directly inside the cell.

Importantly, Kylie's research can be used on thin solar cells. Although thin cells are much cheaper than conventional cells because they use less silicon, they are also much less efficient. "The silver 'antennas' allow thin cells to trap more light, making them much more efficient," says Kylie. "This improvement in efficiency and decrease in cost could help make solar power more competitive with fossil fuels."

Kylie has an undergraduate physics degree from the ANU, winning a University Medal, and a PhD from the ANU as well. She was a Post-doctoral Fellow at the University of New South Wales and the FOM Institute for Atomic and Molecular Physics in Amsterdam.

Kylie says, "I was always interested in science - physics in particular - and I was also interested in environmental issues. Solar cells seemed the perfect combination."

Her beginning in physics gave Kylie some new ideas and insights into the science of solar cells, which has supported her breakthrough developments. From her physics background Kylie brought her understanding of plasmonics to the study of solar cells. Plasmons are density waves of electrons, created when light hits the surface of a metal under precise circumstances. The tiny nanoparticles Kylie places on the top of silicon cells produce the plasmons which direct light into the cells, which is the antenna effect she describes. Kylie is an Australian Research Council Research Fellow and she currently leads the nanostructures for photovoltaics group at the ANU Centre for Sustainable Energy Systems.

In 2010, Kylie's work on nanophotonic light trapping was listed as one of MIT Technology Review's '10 most important emerging technologies'. In 2011 she was an episode winner on ABC television's 'New Inventors'. Her work has been featured in the news sections of Science magazine and The Economist, and she has published over 60 papers, which have been cited over 1000 times to date.

Outside the university Kylie enjoys spending time with her family, and getting out into the many bush areas in and around Canberra.

Wen Zhang is Research Fellow in the Research School of Engineering at The Australian National University.

Wen Zhang's research is all about sound - in particular, the areas of spatial audio and array signal processing.

"Spatial audio means thinking about sound in three dimensions. We design and manipulate speakers so we can change where the listener thinks the sound is coming from," explains Wen.

"Array signal processing is about designing algorithms to improve sonar systems and wireless communications, so that they are better at detecting and locating the source of a signal, or can provide more information about the signal."

Wen's fascination with sound and engineering began at a very early age. When she was six years old she visited a central control room where a telephone operator used a switchboard to put calls through to the right people. "It seemed like magic," says Wen. "I wanted to know how it worked. I remember asking the operator lots of questions."

At school Wen was good at mathematics and problem-solving. "Then when I started university in 1999 the mobile phone boom was getting started. A new telecommunication engineering degree had just become available, so it seemed perfect."

She completed the telecommunication undergraduate degree from Xidian University in China in 2003.

Wen then came to ANU and completed her master of engineering degree in 2005, and her PhD in 2010. The high quality of one of the papers from her studies earned her a grant from the Institute of Electrical and Electronics Engineers (IEEE) Signal Processing Society to attend the 2008 IEEE International Conference on Acoustics, Speech and Signal Processing in Las Vegas.

She was chosen to be a CSIRO OCE Postdoctoral Fellow and worked at CSIRO Process Science and Engineering in Sydney from 2010 to 2012 before joining ANU.

Wen is currently working on an Australian Research Council project investigating 'Robust signal processing theory for synthesis and analysis of spatial wavefields', which can contribute to both spatial audio and array signal processing applications.

She is also working on a subproject to understand more about how humans hear. "We only have two ears, but humans are still surprisingly good at detecting where sounds are coming from. Understanding how we figure this out will be useful for applications such as tracking devices on robots."

Wen really likes academic life. "I just want to keep expanding my knowledge. My career will be in universities where I can keep learning and developing new technologies."

Outside university, Wen's interests are music - "That's why I ended up in spatial audio!" - and dancing.

Steve Blackburn is Professor in the Research School of Computer Science at The Australian National University.

"I started playing with computers as a kid, studied electrical engineering as an undergraduate and then did my PhD in computer science. These two interests have been in balance throughout my career," he says. He became a Professor at ANU in 2012 having done his PhD at the University in 1998.

Steve says his research is about 'abstraction without guilt'. He explains, "Abstraction is the main tool that humans use for combating complexity. The idea is to hide unnecessary detail. You don't need to know how the whole tax system works to fill in a tax return.

"Abstraction is a tool that is used a lot in computer science and engineering, but it usually comes at a cost. That cost can be in terms of time - how fast a program can run, or in energy - how much energy is used by the computer in getting the job done.

"Energy use has become an extremely important consideration in computing, and my main area is studying how software affects energy consumption. I find that this is where computer science and engineering come together, as I need to look at both the software and the hardware.

"'Abstraction without guilt' means being able to produce software that is portable, secure and more reliable, without paying a penalty in terms of performance or energy consumption."

Steve is working on a number of projects in this area, most in collaboration with PhD students. The common theme of the projects is in trying to understand the link between computer languages and new computer architecture.

"Computer hardware is changing rapidly and radically, and seeing how languages interact with this is fascinating. This work has applications in any kind of computing from cell phones to supercomputers, quite literally. One of our projects is with Microsoft looking at how the software used in phones affects their energy usage. Another is with IBM improving the performance of a language called X10 that is designed for supercomputers."

As well as collaborating with PhD students, Steve also works in many projects with industry, including software projects with Google, Microsoft and IBM, and hardware projects with Intel.

Steve has also played an important role in changing how programming language research is actually done. "I set up and led a group of researchers that established new benchmarks for the field," says Steve. "This allowed the international programming language research community to improve and standardise the way they evaluate performance."

Steve says he has never particularly focused on his career.

"I've been too busy just following and researching what I was interested in. What I really want is to make an impact. My goal is that one day I will buy a cell phone and I will know that the chip in it is working as efficiently and effectively as possible because of my group's work - that's the sort of everyday impact I would like.

"My wife is also an academic and we have three kids. My family takes most of my time outside work. I love outdoor stuff and we go hiking. Canberra is one of the best places in the world if you are an academic and if you love the outdoors. I wouldn't be anywhere else."

Lexing Xie is Senior Lecturer in the Research School of Computer Science at The Australian National University.

"Every second, more and more images are put onto the Internet. My work both helps us to understand how people use the new media, and how we can better manipulate and make sense of it all."

Lexing Xie says she was born into an engineering family, and always liked science and engineering. She completed her bachelor degree in electrical engineering at Tsinghua University in Beijing, and master and PhD degrees in electrical engineering at Columbia University in New York City.

"Along the way I converted to computer science, where my research is in multimedia and social media," says Lexing.

"My work is about mining and analysing multimedia data. I use algorithms to analyse large amounts of video, photos, or the comments people make about them.

"This firstly helps us to understand this new media. It can also help us to develop better computer search methods to find images or videos, better tags to support better searching, or better ways to organise the media files.

"We are being presented with an enormous amount of information through our computers. But by developing new software and multimedia platforms we can get computers themselves to help us organise and digest this information."

Lexing came to ANU in December 2010 after a few years at the IBM TJ Watson Research Center. She is currently working on two projects.

The first is looking at image tags - the words that are attached to images to describe and label them. "There are tens of thousands of words in our language, but they are not all equally used in tags," says Lexing. "Understanding which words are used more often can help us to teach computers to make better suggestions for tags or even to automatically generate tags."

The second is analysing video content on YouTube. "Making videos can be difficult and time consuming", says Lexing. "Many people copy material to get around this hurdle - my research in matching videos has found that more than half of the material on the web is copied or repackaged. My analysis can help us understand what becomes popular and why."

"I like the area I am working in and find it very interesting. I would like to think that my work will have an impact - that in 10 to 20 years there will be a technical component of a multimedia platform that has my name on it. I currently have three granted and nine pending patents."

"As a university academic I also like teaching and talking to students. I hope I am having an impact there too - that in 10 to 20 years my graduates remember something that they learned from me."

Lexing says that when not researching she can be found travelling, reading, or playing music badly. She can be also found peeking through her camera. "My research crosses over to my hobbies - I have a decent collection of photos on Flickr!"