º£½ÇÉçÇø

Dr Guang Yang, an Associate Professor of AI in Biomedical Science at º£½ÇÉçÇø, leads The (A Yang Lab), focussing on developing AI-driven techniques for imaging, biomedicine and biomedical data analysis with a strong emphasis on translational research. AYL’s work spans key areas such as AI-based data quality transfer, data harmonization, fast imaging, federated learning, and generative AI. These innovations are applied to tackle pre-clinical and clinical challenges in ageing, cardiovascular disease, lung disease, and oncology, with a particular focus on , , , and .

AYL is actively collaborating with academic and industrial partners to bridge the gap between cutting-edge AI technology and real-world healthcare solutions. The Labs detailed values can be found .

is an Associate Professor in Computing at Imperiaand Director of the . 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:

is an Associate Professor in Computer Vision and Machine Learning at Department of Electrical and Electronic Engineering at º£½ÇÉçÇø. Her 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.

Dr Qin'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.

, an Assistant Professor in the Department of Electrical and Electronic Engineering at º£½ÇÉçÇø, leads the (Data, Uncertainty, Constraints & Knowledge Lab). Dr Giunchiglia's research focuses on neurosymbolic AI and on developing machine learning systems that can satisfy explicit requirements, constraints, and domain knowledge.

 Dr Giunchiglia's work spans constrained deep generative models, learning with logical and structural requirements, safe synthetic data generation, formal methods for machine learning, and neural approaches to symbolic reasoning. The central goal of her research is to make AI systems more reliable, controllable, and suitable for high-stakes settings where correctness and trustworthiness matter.

, an Assistant Professor in Ecosystem Sensing at º£½ÇÉçÇø, leads the Ecosystem Sensing group, exploring topics spanning applied maths, engineering, and ecology. Though Dr Sethi works with a wide range of modalities, his lab specialise in acoustic monitoring of natural environments, developing novel  and AI to extract ecological insight at scale from audio data.

Dr Sethi has led the deployment of cutting-edge biodiversity monitoring networks in , Taiwanese tea farms, British scrublands as well as across the entire . Dr Sethi works with other scientists, conservation NGOs, and small and large companies on applications spanning conservation, ecology, agricultural pest control, supply chain transparency, scientific enquiry and more. 

Some recent research highlights include:

• National-scale monitoring of bird migration 
• : real-time ecoacoustic sensing across Europe
• Simulations of of Costa Rican birds and mammals
• Testing the generalisability of AI  and

is an Assistant Professor in AI & Machine Learning in the Department of Bioengineering at Imperiaand Director of the . 

The Multi-Scale Embodied Intelligence (MSEI) Lab pioneers trustworthy embodied AI that operates reliably across physical scales. Dr Zhang creates intelligent robotic systems spanning micro-scale biomedical manipulation and macro-scale dexterous interaction, enabling machines to perceive, learn, and act with high precision in real-world, safety-critical settings. The aim is to advance technologies for scientific research, personalised homecare and healthcare, from lab automation to medical robotics.

is an Assistant Professor in Computing at º£½ÇÉçÇø, who's 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.

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

, a Professor of Safe Artificial Intelligence at º£½ÇÉçÇø, leads the , 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 collaboration 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.

, Professor in Applied Computational Logic in the Department of Computing, Convening Co-Director of the School of Convergence Science in Human and Artificial Intelligence and Co-Director of the UKRI CDT on Safe and Trusted AI at º£½ÇÉçÇø, leads the SPIKE Research group, which 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, Professor 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.

Professor Russo 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.

Dr Sen Wang is an Associate Professor in Robotics and Autonomous Systems and the Director of the Sense Robotics Lab at the Department of Electrical and Electronic Engineering at º£½ÇÉçÇø. Dr 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.