people
A diverse squad of academics, postdoctoral fellows, and PhD researchers.
We do this work with everyone, not for anyone. Inclusion in science starts by inclusion in the lab, and the diverse mix of minds, backgrounds, and experiences in this squad is what generates our best ideas.
Behind every line of research is a person. Click any card to meet them.
the squad
Xinyu Chenshe/her
- In Silico Trials & VVUQ
I have the personality of an old-school statistician in a modern AI lab — I care about elegant theory and careful assumptions, but I am always happy to collaborate across disciplines, preferably with a good coffee nearby.
My research focuses on statistical machine learning for reliable AI, particularly uncertainty quantification and evaluation, with an emphasis on clear statistical principles, theoretical guarantees, and practical use in medical applications.
Sam Coveney
Group LeaderBHF/UK CEiRSI Transition Fellow
- Agentic Digital Twinning
- Physiology & Disease Modelling
Family, weight lifting, running in funny shoes, and sport aikido.
My research has comprised uncertainty quantification of complex models, calibration of electrophysiology models and cardiac digital twins, and cardiac diffusion tensor imaging.
Yash Deo
Postdoctoral Research Associate at the University of York
- Doctoral students 2020 – 2025
- Visiting scholars Present
- Agentic Digital Twinning
Yash used deep learning with simulated data for medical imaging, showing how synthetic training data can overcome scarce labels to build robust imaging models.
Thesis
Haoran Dou
Research Associate, University of Manchester, UK
- Doctoral students 2020 – 2025
- Postdoctoral fellows 2024 – present
- Agentic Digital Twinning
- In Silico Trials & VVUQ
INTJ
Medical Image Analysis, Generative Model, Digital Twins and Virtual Patients Modelling. During his PhD in the group, he leveraged generative deep learning to build virtual cardiac populations, creating synthetic patients that power more powerful and ethical in-silico trials.
Thesis
Alejandro F. Frangihe/him
DirectorGroup LeaderBicentenary Turing Chair in Computational Medicine
- Real-World Clinical Phenomics
- Agentic Digital Twinning
- Physiology & Disease Modelling
- In Silico Trials & VVUQ
- Trial Ecosystems & Infrastructure
Husband to a brilliant woman and dad to eight wonderfully different humans (four boys, four girls). Off-duty, you'll find me cooking or making music with the family, lost in philosophy of science, or championing neurodivergent thinkers everywhere.
I develop computational methods that make medicine more precise, predictive, and personalised. My work spans two connected frontiers: computational phenomics, which combines imaging, genetic, and clinical data to reveal what makes each patient unique; and in silico science, which creates virtual patients to test new devices and therapies safely before they reach the clinic. Together, these tools help bring better treatments to patients faster — and shape the regulatory science that gets them there.
Ellie Glaistershe/her
- Real-World Clinical Phenomics
Outside of work, I enjoy playing football & staying active, painting, reading, and spending time with my friends.
My research focuses on using deep learning to help improve image registration methods for safe reirradiation – the delivery of a second (or subsequent) course of radiotherapy to a previously treated area. I am working towards developing image registration methods which can handle drastic anatomical differences and incorporate local dose characteristics. This work will initially focus on a paediatric reirradiation cohort before being extended to other patient cohorts.
Ajay B. Harish
Senior Lecturer in Computational Mechanics
- Physiology & Disease Modelling
Integrating experiments, theory, and computer simulations to develop mechanistic models spanning computational mechanics, materials science, and biomedical engineering.
Lara Highamshe/her
Project Officer
- Professional services
I'm naturally curious, creative, and enjoy working with people just as much as I enjoy a well-organised spreadsheet. Outside work, I love live music, pub quizzes, exploring the countryside, and cuddling my cats.
I am a Project Officer supporting the coordination and day-to-day delivery of the group's projects and organisational activities. I manage communications, organise events and meetings, coordinate timelines and administrative processes, and act as a link between different teams across the University.
Mobarak Hoque
Group LeaderSenior Lecturer in Multimodal Agentic AI for Healthcare
- Real-World Clinical Phenomics
I enjoy appreciating life's simple moments and exploring different cultures through everyday experiences. Interacting with people from diverse backgrounds continually broadens my perspective and reminds me of the importance of empathy, curiosity, and human connection in both research and life.
Mobarak Hoque leads research on agentic AI to help clinicians better understand complex and multimodal patient data and improve early diagnosis, personalised treatments, and clinical decision making in hospitals and operating rooms. His research aims to develop reliable AI systems that work as trusted clinical partners, supporting adaptive, efficient, and safer healthcare through close collaboration between clinicians and AI. This research envisions a future where trustworthy AI enhances medical decision making, improves patient outcomes, and supports safer healthcare delivery.
Jinghan Huang
- Doctoral students 2024 – present
- Agentic Digital Twinning
Outside of research, I enjoy cooking (and eating), travelling, playing the violin, and video games.
My work focuses on cross-topology shape generation for virtual patients and foundation generative models.
Thomas Julian
- Doctoral students 2024 – present
- Real-World Clinical Phenomics
When I'm not working, you'll likely find me traveling, soaking up the atmosphere at a music festival, or experimenting with new recipes in a cooking class.
Academic ophthalmologist using artificial intelligence and images of the eye to predict and prevent major health events such as heart attacks and strokes.
Muna N. Kadhom
- Real-World Clinical Phenomics
As a mother of two and a wife who has lived across multiple countries and cultures, diversity is simply my way of life. This rich experience shapes how I connect with every person I meet, making me adaptable, empathetic, and genuinely curious about people.
My research focuses on advancing neuroradiology by developing faster, safer imaging techniques to improve the detection and understanding of neurological conditions, supporting better diagnosis and patient care. Alongside my research, I am committed to education, teaching medical students, radiology residents, and radiographers. I strive to build strong foundational knowledge and clinical reasoning skills across all levels, preparing the next generation of imaging professionals for the demands of modern radiological practice.
Michael Kipping
Programme Manager, UK CEiRSI
- Professional services
I'm the father to three daughters and we love animals! We have 4 cats and a dog. In my spare time, I enjoy trail running, and am involved in running a number of youth groups including chairman of a league club that was set up to support children in a deprived area and leader for youth music and drama group in Leyland.
Programme Manager for CIMIM and UK CEiRSI. Key focus is enabling the use of in silico approaches in health and life sciences and supporting the development of legislation, guidance, standards.
Dibyakanti Kumar
- Doctoral students 2024 – present
- Physiology & Disease Modelling
Like many others, I was inspired by the spirit of curiosity that Richard Feynman embodied. I enjoy solving problems and finding intuitive, elegant, and sometimes witty ways of thinking about them. I also appreciate clever humour, especially the kind found in Monty Python. If you ever have an idea you'd like to discuss, I'd be more than happy to chat over email or in person.
My research focuses on the theoretical foundations of deep learning, specifically within optimization and learning theory. I am particularly driven to bridge these theoretical insights with practice by developing deep learning frameworks for physics-informed models.
Shaokun Lan
- Agentic Digital Twinning
Powered by sunshine, coffee, and good vibes.
3D generative model.
Tian Liang
Love traveling and reading, or just sleeping.
Focusing deep learning-assisted molecular dynamics simulations (machine learning potentials), molecular representation learning, and agentic AI for drug discovery.
Fengming Lin
Research Associate, University of Manchester
- Doctoral students 2020 – 2025
- Postdoctoral fellows 2024 – present
- Real-World Clinical Phenomics
- Agentic Digital Twinning
Passionate about turning medical AI research into practical, scalable solutions, connecting academic innovation with clinical and business value, and helping ideas move closer to real-world impact.
Develops deep learning methods for medical image analysis. Algorithmic strengths include class-imbalanced, fully supervised, semi-supervised, and self-supervised learning, enabling AI to work with limited expert labels and uneven disease distributions. Application areas include digital twin reconstruction, virtual population generation, and AI agents that automate research and clinical workflows for disease modelling, risk prediction, and treatment planning. During his PhD in the group, he developed vessel-tree segmentation and modality-agnostic aneurysm detection, enabling automatic, imaging-independent identification of aneurysms to aid diagnosis.
Thesis
Michael MacRaild
Research Associate at The University of Manchester
- Doctoral students 2019 – 2024
- Postdoctoral fellows Present
- Physiology & Disease Modelling
- In Silico Trials & VVUQ
I work with determination to reach my goals and I strive to be kind and compassionate with others. Outside of work, I enjoy getting outside for hiking, running and sometimes to play football. I also love to cook.
My research focuses on developing computational models for in-silico trials of cardiovascular and neurovascular medical devices. I have expertise in fluid dynamics, fluid-structure interaction, numerical methods, reduced order models and scientific machine learning. During my PhD in the group, I created efficient ensemble simulation methods for in-silico trials of endovascular devices, making virtual testing of stroke and aneurysm treatments faster and more scalable.
Thesis
Shengzhong Mao
- Research software engineers
Enjoy collaborative research and thoughtful discussions.
Graph Neural Networks, Time Series Analysis, Multimodal Learning, Generative AI.
Tingting Mu
Senior Lecturer in Natural Language Processing, Machine Learning and Robotics
Mathematical modelling and optimisation techniques for machine learning and the analysis of large-scale complex data including text, images, and networks.
Anirbit Mukherjee
Lecturer in Machine Learning
Theory of deep learning and scientific machine learning / AI for science applications.
Vijay Nandurdikar
- Physiology & Disease Modelling
- Agentic Digital Twinning
Powered by books, music, travel, and curiosity.
I study the relationship between abdominal aortic aneurysm (AAA) shape, blood flow, and biomechanical risk factors. My work uses computational simulations, synthetic patient cohorts, and data-driven modelling to investigate how geometric variation influences haemodynamic behaviour.
Pawan Kumar Pandey
Research Associate, Department of Computer Science, University of Manchester
- Physiology & Disease Modelling
- In Silico Trials & VVUQ
I enjoy playing chess, reading, and watching films in my free time. I value long conversations over tea or coffee, and strive to follow a dharmic, vegan lifestyle.
Computational modeling of cerebral hemodynamics and the performance of intrasaccular devices to improve treatment planning and outcomes through efficient patient-specific simulations.
Jon Pickstone
Director of Innovation, UK CEiRSI
- Professional services
I like analytical and creative work.
Interim Innovation Director — helping to develop UK CEiRSI, with a focus on strategy, funding, government affairs, marketing, and communications.
Andrew Rowley
Senior Research Software Engineer
- Research software engineers
I mostly spend my time outside of work transporting my children around, but also enjoy ballroom and latin dancing! I also work on the SpiNNaker Neuromorphic project in Manchester.
Senior Research Software Engineer working on the MULTI-X application, to allow cohort discovery from multiple distributed sources and execution of algorithms on data using high performance compute resources.
Olivia Rudy-Murphy
- Professional services
Patryk Rygiel
PhD Candidate, University of Twente
- Physiology & Disease Modelling
I enjoy staying active in my free time, especially in the mountains while skiing, hiking, rope climbing, or bouldering. I'm also interested in urban and nature photography and enjoy reading.
My research focuses on artificial intelligence (AI) for science and health, with an emphasis on geometric deep learning and neural operators for complex physics simulations, including cardiovascular blood flow and automotive aerodynamics. I am particularly interested in developing methods that generalise well in practical settings and scale to large, complex problems, while remaining reliable under limited data through the incorporation of inductive biases such as symmetries and physics-based conservation laws.
Ali Sarrami-Foroushani
Group LeaderAssistant Professor of Cardiovascular Biomechanics, the University of Manchester, UK
- Doctoral students 2014 – 2018
- Faculty 2023 – present
- Physiology & Disease Modelling
- In Silico Trials & VVUQ
Cardiovascular Biomechanics, Cardiovascular Fluid Dynamics, Computational Mechanics, Cardiovascular Medical Devices, In-Silico Trials, Scientific Machine Learning. During his PhD in the group, he developed in-silico clinical trials to assess intracranial flow diverters, using simulation to virtually test brain-aneurysm devices and accelerate safer medical-device evaluation.
Thesis
Zhenrong Shen
Postdoctoral Research Associate, University of Manchester
- Postdoctoral fellows 2026 – present
- Agentic Digital Twinning
- In Silico Trials & VVUQ
Mostly searching, occasionally finding, always seeking.
Deep learning for medical image analysis, with a focus on multi-modal generative AI for virtual patient populations in in-silico trials.
Yidan Xuehe/him
Group LeaderBHF/UK CEiRSI Transition Fellow
- Postdoctoral fellows 2024 – 2026
- Faculty 2026 – present
- Physiology & Disease Modelling
- In Silico Trials & VVUQ
Outside of work, I enjoy reading, hiking, and listening to classical music. I also like playing video games and bridge with friends.
I apply mathematical and computational simulations to virtually assess medical devices before they reach patients. My current work focuses on the deployment of transcatheter heart valves and minimising associated conduction disturbances. I also contribute to a UK CEiRSI pilot study, developing a framework to integrate digital evidence into the regulatory approval of device design modifications.
Arezoo Zakeri
Group LeaderLecturer in Medical Image Computing & AI
- Faculty 2024 – present
- Agentic Digital Twinning
- In Silico Trials & VVUQ
Outside of work, I enjoy cooking and long walks.
Medical image analysis and AI; my research focuses on generating and validating digital representations of patient anatomy from clinical scans and multimodal data, helping improve treatment, in silico medical device testing, and personalised healthcare.
Zherui Zhou
- Real-World Clinical Phenomics
- Agentic Digital Twinning
Seeking truth beyond narratives, and building value beyond trends.
3D vessel reconstruction and generation.
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alumni
Former group members can be found on the alumni page.