Meet the Student - Ana Carolina

 

Why did you pursue MSc AI in the Biosciences programme?

Artificial Intelligence (AI) is transforming every industry, and I wanted to upskill myself in this rapidly growing field. This programme particularly appealed to me because I come from a biology background but realised I enjoy analysing results and working with large datasets to draw conclusions, rather than focusing on the practical lab-based elements. The course has been fantastic as it places a strong emphasis on hands-on experience alongside theoretical learning.

What have been your highlights so far?

One of the main highlights has been the opportunity to go through the entire machine learning process. This practical, hands-on approach has been invaluable in preparing me for a career in the industry. During the first semester, we started learning to code from scratch, which was extremely helpful and made the process far less intimidating. I’ve also enjoyed engaging in critical thinking with my classmates and collaborating to work through information and find solutions.

Have you thought about any particular career paths?

I am still exploring different fields, but I am particularly interested in machine learning and the automation of processes. This programme provides a wealth of data and functional skills applicable to various sectors, such as pharmaceuticals, marketing, and research. I’ve noticed that many industries are keen to hire students with expertise in AI and machine learning. I also really enjoyed collaborating with Kew Gardens on a plant species identification model. In the future, AI could play a crucial role in helping farmers identify crop diseases from leaf images, which is an exciting area to explore.

Can you tell us about your experience of QMUL?

I’ve really enjoyed my time at QMUL and the vibrant campus environment that caters to everyone. Having studied for my undergraduate degree at a different university, I can say that QMUL has been a very positive experience. I’ve learned so much from my classmates, who come from diverse backgrounds—not just from different countries but also various professional disciplines. This diversity makes group discussions particularly engaging, as new perspectives emerge. For instance, not everyone in my class has a background in computer science or biology, and the programme doesn’t require prior coding experience. Additionally, the abundance of PhD opportunities highlights the strong research focus at QMUL.

How have you found the facilities?

The facilities at QMUL are excellent, especially the Careers Service. They offer internships, career taster sessions, one-to-one appointments, and an online platform to help refine your CV. I applied for an internship back in January and had an appointment to review my CV and cover letter, followed by interview preparation. The Careers Service made the process smooth and was very accommodating, even on short notice. I was fortunate to secure a position as a Data Mining intern with Good Mind, a start-up focused on sustainability. My role involves monitoring data and analysing competitors’ offerings. Interestingly, one of the people at the company is also a QMUL graduate.

Have you any tips for those starting this programme or at Queen Mary?

Be curious—ask questions and explore topics beyond the course syllabus to deepen your understanding. The programme encourages proactivity, so take the initiative and make the most of the opportunities available. Attend part-time job fairs, take advantage of the Careers Service, and explore additional resources like the language learning courses, which are very accessible.

What do you think about the teaching and your lecturers?

The course is relatively small, which allows the lecturers to get to know each student individually. They genuinely care about the student experience and not only provide feedback but also check in regularly about the course content. I feel heard because class feedback is taken seriously. For example, when many of us mentioned that we hadn’t studied maths since school, one of the lecturers adapted their teaching, breaking down the content and offering additional support. This creates a two-way relationship, unlike undergraduate studies, where students can actively contribute, ask questions, and engage more deeply with the material.