Converging on Cancer Seminar Series – Dr Stephen John Sammut and Professor Ben Glocker

17 October, 2024, 3 PM-4 PM

We invite you to join us online on Thursday 17th October, from 3-4 PM (BST).

 

The CRUK Convergence Science Centre is a partnership between Imperial College London and The Institute of Cancer Research (ICR). We aim to bring together researchers from different disciplines across both institutions to develop innovative ways to address challenges in cancer research to benefit patients. 

 

This seminar series brings together speakers from Imperial and ICR to present their research and how they use convergence science to answer cancer-related questions. 

Dr Stephen John Sammut (ICR)

Dr Stephen John Sammut is a Clinician Scientist at the Institute of Cancer Research and a Consultant in Medical Oncology at the Royal Marsden Hospital. He leads the Cancer Dynamics Team which uses multiplatform tumour profiling to understand the biology underlying response to anticancer treatments. He has charted the molecular evolution of early breast cancer during treatment with neoadjuvant chemotherapy and developed the first machine learning framework that combined genomic, transcriptomic and digital pathology data from diagnostic cancer biopsies to predict response to therapy. In addition, his work in metastatic breast cancer showed that the adaptive immune system co-evolves with the tumour genome, providing further support to the cancer immunoediting hypothesis.

 

Professor Ben Glocker (Imperial)

Ben Glocker is Professor in Machine Learning for Imaging at the Department of Computing at Imperial College London where he co-leads the Biomedical Image Analysis Group. He is the Kheiron Medical Technologies / Royal Academy of Engineering Research Chair in Safe Deployment of Medical Imaging AI. He also leads the HeartFlow-Imperial Research Team and is Head of ML Research at Kheiron. He is also the Knowledge Transfer Lead of the EPSRC Causality in Healthcare AI Hub. His research is at the intersection of medical imaging and artificial intelligence aiming to build safe and ethical computational tools for improving image-based detection and diagnosis of disease.