The team members at Computational Oncology Laboratory are made up of experienced and dedicated researchers from a wide variety of disciplines and backgrounds. What unites them is an undying passion for learning and discovery. Meet them below.
Consultant clinical oncologist
I lead the Computational Oncology group at Imperial College, based jointly between the Dept. of Computing and Division of Surgery & Cancer.
I have held several local & national positions. I was previously Clinical Lead for QA at NCRAS and a Macmillan Advisor on Cancer data. I currently chair the RCR QI & Audit committee for clinical oncology. I am on the PC for IEEE CBMS.
My name is Aizaan and I’m a UKRI AI4Health PhD Candidate in Artificial Intelligence and Machine Learning.
I’m a Malaysian-born, British-trained physician with previous experience in neurology, medical education, epidemiology, and public health.
My research project looks at using AI to diagnose disease progression in brain tumours from speech data collected from a mobile app, called BRIAN, developed by The Brain Tumour Charity.
Registered radiation oncology registrar
Since early 2020, Dr Williams and I have started to consider a clinical trial to improve the outcome of patients with large brain metastases which is now known as AROMA. I have successfully developed a novel VMAT dosing model which has been proved to be efficient to deliver higher dose to the target volume.
Honorary Research Fellow
I am a consultant paediatric and adult sarcoma surgeon by background. I now work full time on the real-world applications of data science, AI and robotics in healthcare. My focus is the people, process & pathway aspects of digital healthcare. I have a particular interest in the digital transformation of cancer care and work with start-ups, SME and third sector organisations to help enable this.
KERLANN LE CALVEZ
I am a research assistant and have an interest in big data in healthcare, statistics, coding, machine learning and novel techniques developed to have a better understanding of large cohort of patients with multiple events during their anti-cancer treatments.
I am a data analyst with a background in medical biosciences.
I am working on several projects, mainly focusing on research and data analysis in a national brain tumour patient study as well as developing and validating a segmentation tool to segment temporalis muscle from brain scans.
Clinical trial practitioner
I hold a BSc in Biomedical Science and an MSc in Neuroscience (UCL). I have worked within Imperial NHS Trust and College since 2014, working in neuro-oncology, initially as a Multi-Disciplinary Team co-ordinator, and since August 2017 as the Clinical Research Practitioner at Imperial College Brain Tumour Centre of Excellence.
Clinical oncology medical doctor
I am a clinical oncology medical doctor in the North London training programme, and a clinical fellow in Gamma Knife Radiosurgery at the Wellington Hospital in London. My research is in the computational analysis of stereotactic radiotherapy for brain metastases, working towards a postgraduate MD(Res) degree within the department of Computational Oncology. My research topics include multiple brain metastases, radiomics, and brain metastases mapping.
I am a PhD student in the EPSRC CDT in Modern Statistics and Statistical Machine learning at Imperial College London under the joint supervision of Dr Anthea Monod and Dr Matthew Williams. My research interests are in topological data analysis, statistics, and machine learning and my research project investigates combining these methods to study imaging, molecular, and genetics data for GBM prognosis.
My research focuses on exploring decentralised methods for training and explaining machine learning models with a specific application to cancer prognosis. I am also interested in multimodal approaches for modelling the relationship between images and text directly. I am currently a PhD student with the UKRI CDT in AI for Healthcare at Imperial College London under the joint supervision of Prof Francesca Toni and Dr Matthew Williams.
There are many more team members but they are, at the moment, too shy to introduce themselves.