Biomedical Data Analytics News
TIA Spotlight: Kesi Xu
For this week's Spotlight, we would like to highlight the career journey and current work of Kesi Xu, a Postgraduate Researcher in the centre whose work focuses on cell classification for histopathology. Read more.
Reflections on MIUA 2026: Dublin, DCAFA, and Delivering Warwick鈥檚 Big Announcement
30th UK Conference on Medical Image Understanding and Analysis, Dublin (20th 鈥� 22nd July 2026)
I was lucky enough to get accepted for an oral presentation at this year鈥檚 Conference on Medical Image Understanding and Analysis which was the 30th Anniversary Conference and the first time it was held outside of the UK. The only problem being for a die-hard football fan that the day of travel was the world cup final, and we had to gamble on whether England would be there. Read more.
TIA Centre Guest Editors Launch Special Issue on AI in Cancer
We are pleased to share that TIA Centre researchers Adam Shephard and Shan E Ahmed Raza, together with Rose Wang (University of Missouri-Kansas City), are serving as Guest Editors for a new Cancers Special Issue,"Artificial Intelligence in Cancers: Enhancing Diagnosis and Treatment".
The Special Issue invites submissions on topics including computational pathology, medical imaging, radiology, genomics, multiomics, cancer detection, prognostic prediction, treatment planning, and clinical decision support, highlighting the growing role of AI in advancing cancer research and care.
Find out more and submit your work .
Cloning vs Learning in Quantum Computing
, Warwick DCS researchers Nikhil Bansal and , together with (Yale University), explored a fundamental question that lies at the intersection of foundations of quantum theory and computer science.
The No-Cloning theorem says that it is impossible to perfectly clone quantum states. Even if we allow for approximate errors, quantum cloning of unstructured states remains as expensive as fully characterising them, . In contrast, for reasons akin to No Free Lunch Theorems in machine learning, modern quantum learning theory considers structured classes of states and exploits their structure to learn them efficiently. This naturally leads to the question of whether cloning can be easier than learning for these structured classes of states.
In the new work, this question is answered negatively for stabilizer states. The authors proved that imposing this structural restriction does not separate cloning and learning. The authors prove this via a novel connection to , which was recently introduced to the learning theory literature by B. Axelrod, S. Garg, V. Sharan, and G. Valiant. The work constitutes concrete progress towards understanding whether cloning and learning are fundamentally equally hard.
This work was presented at in April 2026, and it will be presented at in June/July 2026 and at in September 2026.