Laura Gongas

Tell us a bit about you. 

I’m passionate about applying technology to create real social impact, particularly in healthcare.  

I was born and raised in Colombia and from a young age I knew I wanted to work in this field after volunteering at a foundation that provided prosthetic legs to low-income patients.  

Seeing firsthand the challenges of the healthcare system in my country led me to study Biomedical Engineering with the goal of improving people’s quality of life. 

During my degree, however, I realised that what truly excited me wasn’t biomechanics but computer vision (AI applied to images), which at the time was still largely unknown.  

I was fortunate to be part of a research group led by a professor who strongly believed AI would play a transformative role in the future, and that belief stayed with me.  

After five years working in industry applying AI solutions, I came to London to pursue an MSc in AI for Biomedicine and Healthcare at UCL and now work as an AI Lead at MedBrief, a UK medico-legal company. 

What inspired you to go back to university after working in industry?   

The field was evolving extremely fast, and I found it increasingly difficult to stay up to date with the latest technologies without refreshing my academic foundations. 

Even while working in industry, I’ve always valued scientific rigour and I realised that to keep doing meaningful work I needed a deeper understanding of the new models, frameworks and techniques being developed.  

I was also curious to learn how other people and companies around the world were tackling the same challenges I was seeing in my day-to-day work across Latin America and to be part of a community of like-minded people who shared similar interests in applying AI to healthcare.  

Why did you choose the AI for Biomedicine and Healthcare at UCL?  

Given my technical background, I was looking for a programme that combined strong technical depth with a clear focus on real-world healthcare applications, rather than abstract or overly broad AI training.  

UCL stood out as one of the few universities offering an MSc in AI applied specifically to healthcare, which made it a very clear fit for what I was looking for. 

Beyond the programme itself, I was drawn to UCL as a top global university where teaching is closely linked to cutting-edge research and where academics are actively involved in research, industry and policy.  

At the same time, being part of an international postgraduate focused community felt especially valuable at this stage of my career, as it allowed me to learn alongside people with diverse professional and cultural backgrounds. 

Can you tell us what a typical week looks like for you?  

At the end of the day, the goal of my role as an AI Lead at MedBrief is to ensure that what we build is not only technically sound but also usable, compliant and genuinely helpful in practice.  

This means supporting medical and legal professionals in resolving clinical negligence cases as efficiently and fairly as possible. My week is then split between strategic leadership and hands-on technical work. 
 

On the leadership and strategic side, I typically: 

• Start most mornings with a SCRUM daily with the AI team to review progress, align priorities and identify blockers early.
• Meet regularly with the CEO, CTO, product manager, heads of departments and sometimes the wider board to discuss upcoming projects, ongoing work or challenges across the company.
• Help decide where AI can add real value to the business, which problems are worth exploring further through proofs of concept and which ideas should not be prioritised.
• Work closely with operational, clinical and legal teams to understand the domain knowledge and constraints that shape our AI solutions, bridging the business and technical sides of the company. 
   

On the technical side I usually: 

• Take ownership of complex or research heavy problems, especially when the team is blocked or when a new approach needs to be validated.
• Work closely with the development team on model integration and with our MLOps engineer on deployment, monitoring and reliability in production.
• Review pull requests to ensure code quality and consistency, while also using them as a way to guide technical direction and support continuous learning within the team.
• Once a month, organise or attend workshops on topics of interest to the AI team, such as new tools, frameworks or models. 

Aside from the technical AI and ML skills, what other expertise is required in a role like yours? 

 In my role, it’s important to understand the business context as well as the perspectives of domain experts in the medical and legal space and to actively collaborate with them.   

Having AI knowledge alone is not enough for the kind of problems we work on, so clear communication and the ability to translate between technical and non-technical teams are essential.  

In practice, this also means understanding what it takes to use AI responsibly in healthcare. Models need to be interpretable, bias has to be actively considered, and everything must work within strict data protection and compliance constraints.  

Just as importantly, you have to plan for things going wrong, by monitoring models closely and having clear fallbacks when they fail, which they inevitably will.  

 All of this directly affects trust, safety and whether these systems can be used confidently in real medicolegal settings. 

What resources did you use at UCL to help you navigate the professional landscape in the UK?  

Through the UCL MedTech Society, I joined a mentoring programme and was matched with Danai Bili, who was working in AI at Johnson & Johnson.  

Our conversations helped me better understand the UK job market, particularly roles in AI for healthcare, and adapt my CV to UK standards, which differed significantly from what I was used to in Colombia.  

The MedTech Society also played a key role in my early professional transition to the UK. Through an opportunity shared within the society, I joined a student-led consulting firm supporting a company looking to enter the UK market with a cost-efficient AI-based breast cancer screening solution.  

This three-month engagement gave me practical exposure to the UK professional environment and allowed me to apply my industry experience in a new context. 

In parallel, through the UCL AI Society, I joined an AI Safety discussion group following the Bluedot (UK’s AI Safety Institute) course. This exposed me to AI safety considerations that are increasingly relevant in industry and that I hadn’t previously worked with. 

I also worked as a Teaching Assistant for the Database Systems course, which provided academic references that later supported my current role, as well as practical exposure to how professional and administrative systems work in the UK.  

What advice do you have for international students who want to work in the UK after graduating?  

Make the most of everything UCL has to offer. The university is full of inspiring professors, supportive staff and active societies.  

Whatever your interests, there is a space for you, and without even realising it, these environments can lead to the experience, network or mentors you need for your next step.  

Being proactive is key. Start exploring opportunities as early as possible, whether through societies, part-time roles, teaching opportunities or projects connected to industry. Having UK-based experience, even a small amount, makes a big difference when entering the job market. 

Finally, don’t be afraid to ask for help. Most people are genuinely happy to share advice, whether it’s about structuring your CV, where to look for jobs, which events to attend or even understanding what roles exist if you’re still figuring out your path. 

Did you have any inspirational teachers at UCL? 

Yes! So many of them. 

Professor Delmiro Fernandez Reyes was the programme director and my advisor. He was a constant source of support throughout my MSc, particularly in helping me shape a UCL experience which aligned with my interests and next professional steps.  

We have since stayed in touch and started collaborating on MSc dissertations with MedBrief, completing our first cohort and planning to continue this academia-industry collaboration. 

What are your favourite memories of UCL?  

One of my favourite parts of UCL was meeting people from all around the world, with different cultures, backgrounds and ways of seeing the world. Being part of such a diverse and international community was something I found very interesting. 

One of my most memorable experiences at UCL was working with Kenza Benkirane on our research paper, which started as a coursework project and later got published at EMNLP.  

The process was challenging but very rewarding and it led to attending the conference in Miami as an author for the first time.  

Being there in person, presenting our work and finally discussing ideas with researchers who had previously only been names in our literature review was incredibly meaningful.  

It made the field feel much more real and accessible, and helped me see myself as part of the global AI research community.  

UCL also opened doors to learning beyond the classroom and partnering with other institutions. Through a professor, I was encouraged to apply to the Oxford ML for Health & Bio course and had the chance to learn from some of the most advanced and inspiring companies and universities in AI for healthcare.  

Meeting peers from around the world, many of them PhD students in the field, was incredibly rewarding and gave me a clear sense of how fast the field is evolving and how many like-minded people are working on similar challenges globally. 
 
The information on this page reflects the graduate’s status at the time of publication (January 2026).