Robin Leman

Tell us a bit about you. 

I’m Robin, an MSc student in Computational Statistics and Machine Learning at UCL. I’m originally from France, but I spent the last six years in Canada, where I studied Physics and Computer Science at McGill University. 

Before moving to London, I worked as a software engineer in the video game industry at studios like Electronic Arts, Ubisoft, and Microsoft Xbox. I got into the habit of launching a blitz game on chess.com whenever I’m waiting for a long compile or a model to train, so don’t hesitate to find me there. 

What inspired you to study Computational Statistics and Machine Learning? And why at UCL? 

I loved game development, but I wanted to pivot toward a field where my work could have a broader impact beyond entertainment. Machine Learning has become a key tool in addressing challenges in healthcare, sustainability, and energy, and I wanted to contribute to that. 

I chose UCL because it sits at the heart of London’s AI ecosystem, with strong ties to the industry and a programme taught by top researchers in the field. 

What have been the highlights of your course so far? 

UCL has some of the best researchers and lecturers in Machine Learning. The programme is well established, with modules taught by the Gatsby Computational Neuroscience Unit and Google DeepMind. The modules are challenging but highly rewarding, with a strong balance between theory and application. 

For me, though, the real highlight has been the cohort. The programme is relatively small, which makes it easy to get to know everyone, form friendships, and meet some truly impressive people. 

Have there been any challenges? 

Returning to academia after several years in the industry was a challenge, but less difficult than I expected. UCL puts a lot of effort into supporting the transition and making sure everyone starts on a strong foundation. 

Some modules are very demanding, both in depth and workload, so staying organised and disciplined is essential. That said, the programme is well worth the effort, and working with classmates and study groups makes a big difference. 

How does university in the UK compare to Canada? 

I find McGill similar to UCL in a lot of ways. Both are city campuses with welcoming and social environments, and a rich diversity of societies and events. Master’s degrees in the UK are more condensed and can feel more intense. 

In Canada, we had more regular assignments and exams, including midterms. The UK system expects more independence, which requires more autonomy and discipline. 

What does a typical week at university look like for you?

Last term, my week was mostly filled with lectures and independent study. On Wednesdays, I co-headed the Machine Learning tutorials for the UCL AI Society. Every week, we run workshops to teach students the basics of ML outside of modules. 

I always try to stay active and exercise throughout the week, whether at the UCL gym or at the nearby swimming pools. UCL has a very open and social atmosphere, with something happening on campus almost every week. 

Have you had any opportunities to continue your professional development? 

Joining societies and co-heading the Machine Learning tutorials have played an important role in continuing my professional development. The UCL AI Society also organises hackathons with industry sponsors and other social and networking events. 

UCL organises a wide variety of career fairs for each field, which makes it easier to connect with companies. The MSc project during the last term gives you the option to work with a professor or with a company, which makes it perfect for people who want to either work in the industry or in academia. 

What would you like to do when you graduate? 

My goal is to work in research, likely starting as a research engineer in industry before pursuing a PhD. I am particularly interested in generative models, especially world models, with applications in embodied AI. 

Alongside this, I am also interested in more theoretical directions, including causal inference and representation learning. 

What advice do you have for students considering CSML? 

This programme has something for everyone, whether you want to work in industry or continue in academia. The curriculum is flexible, so make sure to carefully research each module offered and choose the ones that align with your goals. Be prepared for a highly rewarding but highly demanding programme. 

The concepts are hard but remember that every problem can always be broken down into more manageable chunks. Finally, remember that the degree is only part of the experience. Getting involved in societies and making the most of London adds a lot to the year. 

The information on this page reflects the graduate's status at the time of publication (January 2026).