Aaron Chadha is a PhD student at University College London’s (UCL) Department of Electronic and Electrical Engineering
Why did you choose this institution and course?
After completing BA/MEng Information and Computer Engineering at the University of Cambridge, I wanted to further pursue my interest in robust and scalable computer vision systems.
I therefore applied for a Biotechnology and Biological Sciences Research Council (BBSRC) CASE studentship in image and video indexing and retrieval, at UCL and in partnership with British Academy of Film and Television Arts (BAFTA) Media Technology, for whom I’ve subsequently designed and built a video retrieval system.
I was also awarded an Industrial Fellowship by the Royal Commission for the Exhibition of 1851, which has provided me with additional funding and resources to attend relevant events, interact with peers and further my research.
What does the programme involve?
My PhD involves researching and developing novel methods for the efficient, high-speed analysis of big video data. The aim is to enable a large database of videos to be analysed in real-time. We want to be able to identify videos that contain content matching a database query, plus assign each video to a class based on its content.
My work requires the exploration of concepts presented in relevant journal papers to develop new ideas that could potentially improve the accuracy and/or efficiency of current approaches. All new ideas must be tested; so far, I’ve written two papers on my findings.
What are the most enjoyable and most challenging aspects of the PhD?
The most enjoyable aspect is working in both design and implementation. I’m not only exploring my field and developing new ideas, I’m also building these into systems with tangible results.
The most challenging part is the lack of a syllabus. The fact that it’s up to me to look for problems worth solving can feel quite daunting, but knowing that I’m pushing the field in a positive direction is very rewarding.
What are your plans for after graduation?
I’d like to remain in the field of computer vision and artificial intelligence. We live in exciting times, with deep learning offering scalable performance in a diverse array of applications from chatbots to self-driving cars. I’d prefer to work at the forefront of such developments.
What advice would you give to those considering PhD study?
Ensure that you’re doing a PhD for the right reasons - expanding your knowledge in a field of interest - and gain experience of the research environment first, perhaps through a summer internship.
In terms of finding problems to work on, stay focused and don’t try to solve everything. I’d also recommend attending relevant events, as these provide the opportunity to interact with others who are working on similar problems, and gauge potential hot topics that may be worth exploring.