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Research Groups

Our research programme at I-X focuses on ambitious ideas with the potential to deliver benefits to humanity and scientific exploration. We focus on both foundational AI and AI applications, including work on pressing problems in health, sustainability, economics, and defence. Foundational AI research focuses on advances in AI technologies such as human-AI interaction, quantum computing, and explainable AI.

We have established a growing research community with over 100 academics participating in more than 30 research initiatives, and a series of new hires bringing fresh ideas and experience to priority areas. Our research groups are at the forefront of cutting-edge research in AI and related fields, and are led by some of the brightest minds at Imperial.

Dr Cristopher Salvi

Dr Cristopher Salvi’s research interests are in rough path theory, signal processing, deep learning and kernel methods. Rough path theory describes the interaction between highly oscillatory signals and non-linear dynamical systems, which has a big impact in the field of stochastic analysis. Dr Salvi is interested in how machine learning techniques can be applied to rough path theory to create advances in the field as part of the Rough Paths Interest Group which is partnered with The Alan Turing Institute and DataSig. Dr Salvi also works on developing algorithms for learning with high-dimensional irregular time series data as well as building deep learning and kernel-based models which can be applied to solve problems in physics, engineering and quantitative finance.

Currently, Dr Salvi is supervising PhD student Nicole Muca-Cirone.

Dr Anastasia Borovykh

Dr Borovykh and her research group work on computational models to understand and improve information processing and learning in artificial and biological intelligent systems through a combination of tools from stochastic processes, statistical mechanics and mathematical modelling. Dr Borovykh applies these methods for the design of privacy-preserving machine learning frameworks. Privacy preserving machine learning frameworks process sensitive data, such as health data. These are vulnerable to attacks and require analysing which models are most at risk and understanding how data is stored within them.

Dr Anastasia Borovykh is also interested in applying machine learning methods for solving various challenges in the field of neuroscience.

 

Dr Chen Qin

Dr Qin’s research is interdisciplinary in nature and at the intersection between machine learning and medical imaging, with a vision towards improving medical imaging workflow via machine intelligence for significant impact in clinical use. Chen’s current research mainly focuses on the development of robust and trustworthy machine learning algorithms for medical image computing and analysis, including MR image reconstruction, medical image segmentation, image registration and motion tracking. Currently, Dr Qin is working on clinical applications of medical image computing in neurology and cardiovascular.

Dr Nicole Salomons

Dr Salomons’ research focuses on developing robots that shape human interactions throughout day-to-day tasks in complex and dynamic environments. The main area of Dr Salomons’ interest lies in creating peer robot tutors that can provide effective teaching.

Nicole does this by building robotic systems that create skill models of users while they perform complex tasks so the robot can provide personalized feedback for long-term skill acquisition. These models are able to select the best tasks for a user to maximise their learning. Dr Salomons’ work has demonstrated the effective use of robotic tutoring in a variety of domains including electronic circuit building, social skills training for children with autism, and in-home exercise coaching. This includes developing in-home long term systems to help shape positive behaviours which encourages both collaboration between both the user and the robot and between the user and other family members.

Additionally, Nicole studies how groups of robots influence users’ decisions. Dr Salomons’ research has demonstrated that a group of robots can cause informational and normative conformity and that groups of robots can induce prosocial behaviour in people.

Dr Roberto Bondesan

The goal of Dr Bondesan’s research is to understand how quantum computing and machine learning can help solve hard computational problems that occur in science and engineering, such as combinatorial optimization problems and the simulation of quantum systems. From finding the shortest route across cities, to finding the best way to design a complex system on a chip, combinatorial optimization problems are ubiquitous in the real world, while the efficient simulation of quantum systems will aid the discovery of new molecules and materials, with applications to drug design and sustainability.

Quantum computers can simulate quantum systems exponentially faster than classical computers and can speed up the solution to combinatorial optimization problems. Dr Bondesan’s team studies novel quantum algorithms and the challenges of deploying quantum algorithms to hardware, such as quantum error correction.

ML algorithms can learn automatically from data to approximate the solution to optimization and quantum physics problems. Data in these domains is however scarce and expensive, and Dr Bondesan’s team focuses on data efficient learning for these applications, from the design of equivariant neural architectures, to model based reinforcement learning, to neurally-augmented Monte Carlo simulations.

Dr Lukas Gonon

Computer code on a monitor.

Dr Gonon’s research is at the intersection of mathematics, machine learning and finance. It centers around various machine learning methods (deep learning, reservoir computing, random features, kernel methods, …) and their applications to stochastic processes, partial differential equations and mathematical finance. This encompasses applying and refining these methods or developing novel methods for practically important applications (e.g. hedging or financial bubble detection) and carrying out mathematical analyses (e.g. proving bounds on the approximation errors of deep neural networks for pricing) in order to gain a more profound theoretical understanding of these methods.

Dr Sen Wang

Dr Sen Wang’s research sits at the intersection of robotics, computer vision and machine learning, driving robots and intelligent machines to understand and operate autonomously in unstructured, dynamic environments through probabilistic and learning approaches. Dr Wang’s main research areas include robot localisation, autonomous navigation, SLAM, robot vision, robot learning and their applications to tackle societal challenges from climate change to healthcare. Dr Wang was awarded the 2022 AI Most Influential Scholar Award Honourable Mention in Robotics and serves as Associate Editors of IEEE Transactions on Automation Science and Engineering, IEEE Robotics and Automation Letters, ICRA and IROS.

Dr Islem Rekik

Dr Islem Rekik is the Director of the Brain And SIgnal Research and Analysis (BASIRA) laboratory and Associate Professor at Imperial College London (IX, Computing). Together with BASIRA members, Dr Rekik has conducted more than 80 cutting-edge research projects, published in high-impact journals and conferences, and cross-pollinating AI and healthcare —with a sharp focus on brain imaging and neuroscience.

Dr Rekik is also a co/chair/organizer of more than 14 international first-class conferences/workshops/competitions (e.g., Affordable AI 2021-22, Predictive AI 2018-2022, Machine Learning in Medical Imaging 2021-22, WILL competition 2021-22). Islem is the former president of the Women in MICCAI (WiM) and the co-founder of the international RISE Network to Reinforce Inclusiveness & diverSity and Empower minority researchers in Low-Middle Income Countries (LMIC) in the field of medical imaging and AI. Dr Rekik is a strong advocate for EDI and AI capacity building in Africa and beyond.

For more about the BASIRA Lab, see:

Professor Alessandra Russo

Professor Russo contributes to AI research by developing innovative symbolic machine learning algorithms and systems capable of automatically learning complex predictive models from data and domain-specific background knowledge. These models are, importantly, explainable in human terms.

Explainability is a main advantage of these models, further facilitating a closer interaction between humans and the machine. In collaboration with her PhD students, Dr Russo has established a new form of symbolic machine learning which is proven to subsume all existing state-of-the-art symbolic machine learning systems, to be able to learn optimal solutions, to be robust to noise in the data, by overperforming differentiable symbolic learning approaches and which benefit from proof guarantees about soundness and completeness of the learned models. Alessandra has successfully developed novel neuro-symbolic learning approaches that integrate her symbolic machine learning systems with reinforcement learning and deep learning methods. A second line of research contribution has been the application of these AI solutions to domains such as software engineering, security, privacy, and network management.

Professor Alessio Lomuscio

Professor Alesso Lomuscio leads the Safe Artificial Intelligence Lab (SAIL), which focuses on the development of methods and tools to verify AI systems for their safe and secure deployment in practical applications. The research initiative, in collobaration with the Assured Autonomy DARPA programme and the Centre for Doctoral Training in Safe and Trusted AI, applies novel approaches such as machine learning to verify neural and autonomous systems (e.g. autonomous vehicles and robotic systems) and contributes to research in logic-based verification of multi-agent systems and parametrised verification of robotic swarms. The VAS group also aims to improve the explainability and fairness of AI systems for the future of safe artificial intelligence.

Dr Lomuscio’s lab has a history of development of open-source tools for safe AI in collaboration with both members of academia and industry partners in an effort to make artificial intelligence safe and secure for society to use.

Professor Hamed Haddadi

In general, Professor Hamed Haddadi’s research interests are User-Centered Systems, Internet of Things, Applied Machine Learning, Privacy, and Human-Data Interaction. Currently, Dr Haddadi is working on several EPSRC funded projects including the Open Plus Fellowship and the PETRAS ISPEF Fund which cover several of these interests. The Open Plus Fellowship, along with partners in industry, aims to provide private, trusted, personalised, and dynamic models on consumer devices that cater to the user’s requirements. This is increasingly in demand as the Internet of Things expands and consumers desire personalised products but also the protection of their privacy. The PETRAS ISPEF Fund supports the PRISM project which collaborates with the NHS and UK Dementia Research to develop an interface for users in the healthcare industry to provide privacy-preserving monitoring and home care. Dr Haddadi also contributes to several other projects as well as working in his industrial role as Chief Scientist of Brave software to increase the security of the Internet of Things and preserve user privacy.

Dr Sarab Sethi

Dr Sarab Sethi leads the Ecosystem Sensing group at Imperial College London, exploring topics spanning applied maths, engineering, and ecology. His work focuses on acoustic monitoring of natural environments, developing novel real-time sensing devices and AI to extract ecological insight at scale from audio data. Dr Sethi has led the deployment of cutting-edge biodiversity monitoring networks in Borneo’s tropical forests as well as across the entire span of Norway. Applications of his work include conservation, agricultural pest control, supply chain transparency, scientific enquiry, and more.

Dr Guang Yang

The AYL (A Yang Lab) at Imperial College London, led by Dr. Guang Yang, focuses on developing artificial intelligence (AI)-powered applications for biomedicine, including Smart ImagingBig Data Analysis, and AI in Drug Discovery. Our Values can be found here.

Dr Patrick Dunne

Dr Patrick Dunne leads the I-X Neutrino research group, which explores the importance of neutrino particles. These rarely interacting particles could hold the key to why the universe is made of matter and not antimatter. Dr Dunne focuses on studying the differences between matter and antimatter in the oscillatory behaviour of the neutrino as it travels long distances.

Dr Dunne’s team uses Bayesian techniques like Markov chain Monte Carlo to analyse the large datasets from these particle physics experiments. Dunne’s MaCh3 analysis framework is in use for current experiments like the T2K experiment in Japan and joint analyses of data from T2K and the NOvA experiment in the USA which will produce the strongest measurements of these matter-antimatter differences this decade. MaCh3 is also used for sensitivity studies of the future argon-based DUNE experiment in the USA which will have the sensitivity to definitively confirm and precisely measure this phenomenon. As well as analysis Dunne’s team is designing and building high-throughput electronics for DUNE’s phase II near detector, with Dunne also leading the experiment’s phase II near detector working group.