Christopher P. Bridge

Researcher in Biomedical Image Analysis

Photo of Chris

I grew up in Essex in the south of England. I became interested in Engineering whilst at secondary school at King Edward VI Grammar School, Chelmsford, and joined the school’s thriving Young Engineers club, with whom I competed in a number of regional and national competitions.

In 2009, I began studying on the Engineering Tripos at Pembroke College, University of Cambridge. I chose to specialise in Information and Computer Engineering in the final years of the course, and developed particular interests in the areas of control theory, signal processing, image analysis, and machine learning. In my final year, my research project involved applying this knowledge to the problem of registering human femur surfaces extracted from CT scans, as part of a pipeline to improve our understanding of hip fracture. This project inspired me to enter the field of medical image analysis as a particularly challenging and rewarding application of the skills I had learnt.

In my summer vacations I completed three summer internships at Selex Galileo (now Selex ES), a company specialising in electronic systems, particularly for defence and security applications. This experience was invaluable for improving my software engineering skills.

I graduated from Cambridge in 2013, with the B.A. and M.Eng. degrees awarded with Distinction. During my degree I was awarded First Class Honours in all years of the course, College and Foundation Scholarships from Pembroke College, a prize for best final year research presentation within the Information Engineering division, and the AT&T Prize for achieving the best overall final-year performance of students in the Electrical and Information Engineering areas.

After graduating from Cambridge, I began my DPhil (aka PhD) at the University of Oxford’s Institute of Biomedical Engineering as a student of Balliol College, Oxford. I was supervised by the Institute‚Äôs director, Professor Alison Noble, and worked on the application of machine learning techniques to medical ultrasound. In particular I was working to develop software that can develop an understanding of video streams of 2D ultrasound data of the fetal heart. I successfully defended my DPhil thesis in July 2017.

In August 2017, I began working as an Innovation Fellow, and then more recently as a Machine Learning Scientist and Senior Machine Learning Scientist, at the Massachusetts General Hospital and Brigham and Women’s Hospital Center for Clinical Data Science in Boston, USA.

Outside of my academic work, I play guitar, clarinet, and saxophone in various musical groups. I also enjoy badminton and running.

You can find my short form CV here.