Dr Stamatia Giannarou
Lecturer in Surgical Cancer Technology and Imaging, Department of Surgery & Cancer
Every successful operation depends on surgical skill to navigate the body. Pre-operative scans play a critical role, guiding surgeons like a map. But these are just snapshots. What if the map could be updated live – and even zoomed in and out? Real-time, multi-scale imaging would give surgeons a ‘sat nav’ to precisely identify tissues, protect critical organs and even see beyond their scalpels before a cut is ever made.
Placing sensors into surgical tools, and fusing data from multiple sources, promises an entirely new imaging toolbox for the most delicate cancer and neurological operations. Equipped with these tools, future human – and robotic – surgeons will be able to ensure that even complex tumours are completely removed at a microscopic level, improving outcomes and safety.
I’m curious about…“how robotics and imaging can together enable safer, more effective brain surgery”
Dr Stamatia Giannarou is a Royal Society University Research Fellow at the Hamlyn Centre for Robotic Surgery, Imperial College London. Matina holds an MEng in Electrical and Computer Engineering from Democritus University of Thrace, an MSc in communications and signal processing and a PhD in object recognition from the department of Electrical and Electronic Engineering, Imperial College London.
Matina’s work spans robotics and computer science in order to develop better robotic vision for surgical navigation. Her fellowship focuses on two key questions:
Published in 2016
Dr Billy Wu
Senior Lecturer, Dyson School of Design Engineering
3D printing has grabbed popular imagination. But the polymer printers widely available today are not the technologies that will transform manufacturing and mass customisation of products. If 3D printing is to realise its potential to produce parts that can compete on stength, durability and functional properties, we will need printing methods that can use a wide range of metal alloy, composite and organic substrates. To optimise the production of lightweighted parts with novel geometries will require new computer modelling tools. And if 3D printing is ever to work on a nanoscale, to create functional materials such as batteries, then we may need entirely new material deposition techniques. As we start this journey we can be sure of one thing: the 3D printers of tomorrow will not look anything like the printers of today.
I’m curious about… “being able to print a fully functional battery from scratch”
Dr. Billy Wu is a senior lecturer at Imperial College London in the Dyson School of Design Engineering where he works on electrochemical devices and manufacturing. Within the School, he is the part of the Energy Technologies and Systems theme and also jointly leads the Electrochemical Science and Engineering group. Billy received his PhD from Imperial College London on modelling and testing of proton exchange membrane fuel cell hybrid powertrains for electric vehicles and completed post-doctoral research on large-scale energy storage systems.
Billy’s research focuses on additive manufacturing (3D printing) and electrochemical devices. His work explores:
Published in 2016
Dr Ben Glocker
Reader in Machine Learning for Imaging, Department of Computing
Behind the 2015 victory of an artificial intelligence against Fan Hui, Europe’s reigning champion player of the board game ‘Go’, lies a new generation of deep learning algorithms that are helping computers to perform ever more complex tasks. Visual perception is one area where humans excel, but what if a machine could match or even exceed our ability to discriminate objects and identify patterns in what we see?
Applied to complex medical imaging problems, such as quantifying damage after traumatic brain injury, deep learning methods using artificial neural networks are breaking new ground. AI radiographers – powered by intelligent algorithms – will soon complement human skill, guarding against medical errors and freeing doctors to focus on the most important decisions. But this is just the start – techniques that allow machines to develop a semantic understanding of image data by analysing very large datasets are already showing promise in identifying disease-related patterns and phenomena entirely new to medicine.
I’m curious about…“the new medicine waiting to be discovered in the medical scans we take today”
Dr Ben Glocker is a Reader in Machine Learning for Imaging at the Department of Computing, Imperial College London. He holds a PhD from TU Munich, and was a post-doc at Microsoft Research Cambridge and a research fellow at the University of Cambridge. Ben is the co-lead of the Biomedical Imaging Analyis (BioMedIA) group at Imperial College. In 2016, Ben was nominated as a World Economic Forum Young Scientist.
Ben’s research focuses on applying machine learning techniques for advanced biomedical image computing and medical computer vision, spanning three disciplines:
Ben asks: Can we automatically extract clinically useful information from medical images in order to support clinicians as they work to improve diagnosis, therapy and treatment?
Published in 2016
Dr Gabrielle Thomas
Academic Visitor, Department of Physics
From the discovery of DNA to the identification of water on Mars, lab-based spectroscopy is fundamental to understanding the chemical composition of matter. But what if you could apply spectroscopy not to small samples but on a planetary scale? Imagine a sensor small and powerful enough to be placed in a micro-satellite but which can assess the chemical state of an entire ecosystem with centimetre precision.
New laser-based methods combine the ability to rapidly survey large areas with the diagnostic capabilities of spectroscopy, revealing not only the types and extent of resources but the health of living reserves such as plants and forests. The technology sets the stage for remote monitoring that will transform agriculture and land management, and support government and humanitarian efforts to manage and conserve resources across the globe.
I’m curious about… “whether precision agriculture could be a service accessible to everyone on the planet”
Dr Gabrielle Thomas is a freelance scientist based in Berlin, currently working as a consultant for M Squared Lasers, and an academic visitor in the Photonics Group, Department of Physics at Imperial College.
She was recently awarded a Marie Skłodowska-Curie fellowship, to be undertaken in the Center for Laser Materials (sponsored by Dr Christian Kränkel) at the Leibniz Institute for Crystal Growth (IKZ) in Berlin. This project (ErMIR) involves the development of cascade erbium-doped lasers for remote sensing applications.
Gabrielle’s research aims to develop beyond-state-of-the-art laser sources that revolutionise a broad range of applications, including:
Published in 2016
Dr Katharina Hauck
Reader in Health Economics, School of Public Health
The decisions that policy-makers take often command substantial resources and can profoundly shape the lives of the people they govern. When it comes to visionary aims, such as improving the health of entire populations, decision-making is beset by challenges. The sheer range of opportunities for action, uncertainty about future states of the world, and the dynamic, long-term interactions of factors that influence change are major barriers to robust, evidence-informed policy-making.
Now, a new generation of economic modelling based on high performance computing is addressing these gaps. Rather than modelling the expected benefits and costs of decisions using just a single model, we can now simulate future impacts under thousands of alternative scenarios, integrating large volumes of data from disparate sources. By assessing the impact of choices and their resilience to different scenarios, policy-makers are better equipped to make decisions that are robust under many alternative futures. Models can be updated in real time to reflect changing realities, new evidence or changes in policy objectives.
I’m curious about…“our ability to model the outcomes of complex, real-world decisions and create new tools for governments and businesses”
Dynamic modelling allows us to design cost-effective healthcare interventions, to predict the impact of changes in individuals’ behaviour and life style choices, to improve our preparedness for future pandemics, and to design effective public health interventions that reduce the burden of communicable and non-communicable diseases around the world.
Dr Katharina Hauck is a Reader in Health Economics at the Department of Infectious Disease Epidemiology, Imperial College London. She joined Imperial College with a PhD in Economics from the University of York following appointments with the Department of Econometrics and Business Statistics at Monash University, the Centre for Health Economics, University of York, and at the World Health Organization in Geneva.
Katharina’s research focuses on empirical health economics, the quantitative evaluation of health policy, and the economics of infectious diseases:
Katharina is an expert in econometric and statistical modelling, micro-simulation and systems dynamics modelling.
Published in 2016
AI and its application are being hyped and discussed across a range of industries. These new technologies are helping researchers explore fundamental processes in chemistry and biology from photosynthesis to the development of new molecules. As new technologies impact on scientific discovery and society more broadly, we will begin to see more interesting symbiotic relationships between AI and humans. In a session at Tech Foresight 2038, Professor Mimi Hii and Dr Mark Kennedy discussed whether we should outsource our discovery to AI and what it might mean for the future.
Dr Neil Alford
The Associate Provost for Academic Planning and former Vice-Dean
for Research in the Faculty of Engineering at Imperial College London