Could you tell us a little about yourself and about your studies before coming to Imperial?

I am from Bulgaria and I came to school in the UK when I was 12.  I was always curious about maths and science, so I ended up studying Physics at Warwick.  I enrolled in some scientific programming courses throughout the degree which initially sparked my interest in computing, but apart from this, my programming experience was minimal.  I also did various technology internships within finance to expose myself more to the world of technology, but I wouldn't say that these were detrimental in helping me out during the MSc!

What attracted you about the MSc in AI?

I was originally planning to do pure computing, as a conversion course, but the summer before the degree started, I started to think whether there wasn't a particular area that I wanted to focus on.  At that time, I was very interested in the brain and I wanted to explore the intersection between computing and intelligence, so AI made sense for me.  However, I wasn't sure that my programming base was good enough to completely jump ship.  The MSc AI had a very focused aim of helping STEM students transition into the field, which I thought was very well suited for my background and what I wanted to work on in the future—this was something unique about the degree.

What did you enjoy the most?

In contrast to my huge physics cohort at Warwick, the MSc AI cohort size allowed you to get to know people very well.  You work with and help each other all the time, and everyone is interested in similar things as you, so the conversations are always fun.  I think, looking back, that the critical moments, when we were closer to deadlines or during examinations, were actually the best.  It was extremely motivating to work so intensely and closely with other people, particularly on any of the group projects.

What did you find more challenging?

I took the Probabilistic Inference module, and found it very challenging—but in spite of finding the material and exam hard, it was actually the module which I liked the most!  It's very applicable now in some areas of my work, so I'm glad I took it.  The individual project was also quite challenging—you are expected to work independently for a very long period of time and sometimes that was difficult.  Probably COVID-19 didn't help!  Having said that, I do think that quite often you need to have a self-reinforced feedback loop, so being challenged in that way can also be quite useful for personal motivation.

Could you tell us about some of your achievements on the MSc that make you proud?

One of the courses is the Software Engineering Group Project, where we created an interface which helps clinicians diagnose Crohn's disease using deep learning.  Apart from the fact that the whole experience was very fun, I am quite proud of the product we delivered.  I think we improved 70% of the final product in the last 2 weeks before the deadline, but because we were finding many unfinished parts, we were not prepared to just leave them as they were.  We wanted clinicians to be able to trial the product by the end so we worked tirelessly to ensure that everything was completed, and I think it paid off.

What did you do in your spare time?

I really liked reading and listening to podcasts.  Podcasts were how I heard about many of the most influential researchers in AI in the first place.  People might know about Lex Friedman—he usually has very interesting discussions about AI.

Could you tell us about your individual project?

I worked with data from the Imperial Centre for Psychedlic Research to decode neural signals from patients on psychedelics who were watching videos of natural scenery.  What we were essentially trying to do was to recreate the computations in the brain, using machine learning models, as a way to understand brain functionality better.  It was a great experience to understand what academic research would be like, as you dedicate around half of your time on this and it is very in-depth.  This was not one of the projects initially offered on the projects list, but because I was so interested in how the brain works, I think it shows that whatever you are into, can be made available for you to explore further.

What have you been doing since you graduated?

I have been working in a systematic hedge fund as a quant researcher, where I focus on developing and analysing models to include in the trading strategies.  I have a lot of fun because your models get to compete on a global level, which is motivating as you need to innovate quickly to stay competitive.  In terms of the data that we work with, we look at many stocks in relation to one another, rather than using a time-series approach, so the datasets are very high-dimensional, which is where skills from the degree come in quite useful!

Do you have any advice for prospective students?

First—read more!  Particularly in areas which you are interested in.  AI is such a relevant topic which is present everywhere and is shaping the world.  Secondly—speak to people.  I think this can be so undervalued, but speaking to as many people as you can at the start of the degree, to figure out which area interests you the most, is one of the most productive uses of time.