Ece Okutan

Tell us a bit about you  

I’m Ece, a second-year Computer Science student here at UCL with a minor in Robotics. I’m originally from Istanbul, Turkey. 

I’m interested in exploring how data and automation apply to different areas in life, with a special interest in how it manifests in motorsport, which has shaped a lot of my projects. 

I’m also the Formula Student AI Lead for UCL Racing, where I founded and now lead the autonomous sub-team.  

Why did you choose to study Computer Science at UCL? 

I had been involved in software development and machine learning since early high school, so I knew that I enjoyed the world of computer science and the prospect of building things, and I wanted to continue exploring. 

UCL stood out to me with how hands-on the learning is, while also providing a very in-depth technical background. The IXN programme was also a big draw, where UCL provides its students with the opportunity to build real things with real stakeholders. The idea of working with industry partners as part of your degree felt very different from what other universities offered. 

What has been the biggest surprise about your course and why? 

I was very surprised by how much there is to learn outside of traditional lectures. I joined UCL Racing in my first year and ended up founding an autonomous driving sub-team, learning ROS 2, and writing software that’ll run on a real car at competition.

Through IXN, I learned how to define a project structure from scratch, make architectural decisions with real consequences, and iterate on them with an industry partner.

None of that is in any traditional module handbook. The degree gives you the foundations, but UCL’s environment, the societies, the people, and the resources give you the space to go way beyond that. 

What did you do for your IXN project? 

My team and I built F1 Jarvis Granite, supervised by IBM and the UCL School of Pharmacy. 

F1 Jarvis Granite is an AI-powered race engineering and telemetry analysis platform that ingests real-time telemetry data from the racing simulation Assetto Corsa, consisting of 3 parts: Jarvis Live, Jarvis Post, and Jarvis VR. 

For Jarvis Live, we plot real-time telemetry graphs (similar to those you might have seen in F1 pitwalls) and offer an AI race engineer that alerts users of specific events based on provided data and answers the driver’s questions through voice interaction. 

With Jarvis Post, the user can then scrub through recorded sessions, review configurable telemetry graphs, replay racing sessions on the track map, and access our AI analyst, which provides a technical breakdown of performance, and our AI coach, which provides prioritized, actionable driving improvement tips. 

The VR environment was built as an educational environment. Essentially, we worked to give a sim racer the same kind of data-driven feedback a real F1 engineering team provides to its drivers. 

What was the biggest challenge you faced with IXN and how did you overcome it? 

Limiting our scope was a challenge. We wanted to do so much in the limited time we had, and we struggled to decide which functionalities to prioritize. 

It took us some time to accept that some things would have to be future improvements, such as supporting multiple simulation games and creating the hardware connection to the UCLR Formula Student car to ingest data from physical cars as well. 

We overcame this issue by arranging a team meeting to simply sit down and talk about which functionalities were a “must” and which were “could be cool.” It was almost like an auction of ideas, where we brought up each one by one and talked about the impact and the time it would take, ultimately coming to a conclusion of priorities, and sadly having to drop some ideas. 

What skills did you develop through IXN that you might not have with regular modules? 

Leading a team and creating a product from scratch. In regular modules, we usually have a very strict guideline of what exactly is to be submitted. With IXN, we had a lot of creative freedom throughout the entire project, which was difficult but very rewarding. 

I learned how to scope features, define priorities with an industry partner, research the existing market, and present technical work to non-technical audiences. 

I got so much better at structuring a project fully and end-to-end system design, things you don’t normally encounter, especially to this extent, in a typical coursework setting. 

How have the first two years of your degree shaped your career aspirations? 

They have made me so much more certain that I want to work at the intersection of software engineering, data science, and physical systems. 

Before UCL, I knew that I enjoyed robotics, ML, and software development in the abstract, but I never noticed how many different ways they can interact! 

Throughout my time at UCL, I always looked out for opportunities for myself to immerse myself in areas that interest me. Through my courses, UCL Racing’s Formula Student team, IXN, and solo projects, I got more and more confident that I wanted to build software that interacts with the real world! 

How different do you feel from the girl who arrived in London for Fresher’s Week in 2024? 

Very! Most notably, I learned how to take care of myself in every sense. Living alone in a new city for the first time, you figure out the basics pretty quickly, such as cooking, cleaning, and managing your own time. 

The same independence translates into how I work now, too. I take ownership of things differently. When something breaks at 2 am, it’s on me to fix it, whether it’s my coursework code or my kitchen appliances. That sense of responsibility didn’t exist when I first arrived in London. 

What advice do you have for other international students considering UCL Computer Science? 

If you have an idea that you want to pursue, pursue it! UCL is very supportive of new ideas and development. 

For example, the Formula Student AI team was simply an idea until I pursued it by creating a proposal document and pitching it to relevant people, getting their support, and making the idea an actual team within UCL! 

Another example is when I wanted to gain more hands-on experience with robotics. I researched Robotics & ML labs at UCL, contacted a professor asking to get involved, and was given a research opportunity! 

Don’t be afraid to reach out to professors or staff; they are more than willing to support you!

The information on this page is the view of the student and reflects their experience at the time of publication (April 2026)