Lara Masad (MSc Data Science and Artificial Intelligence (Conversion), 2025)

Why did you choose to study your programme?

After 12 years leading analytics and AI in the pharmaceutical industry, I had become a confident practitioner. But I wanted more than that. I wanted to understand how intelligent systems are actually built, the mathematics behind the models, the assumptions behind the decisions, and the responsibility behind the outputs.

The MSc, which I undertook as a Chevening Scholar, gave me that foundation with rigour I did not take for granted. It was late nights after very long days. It was choosing growth, again and again. And standing on that stage in London, hearing my name called with Distinction, made every moment of it feel exactly right.

Why did you choose to study at Queen Mary?

I never met my grandfather. But I grew up hearing how deeply he believed in education, so deeply that decades ago, he sent my father to study in the UK. Years later, I stood on the People’s Palace stage in London receiving my MSc as a Chevening Scholar. That moment felt bigger than a degree. It felt like a continuation of a story my grandfather had started long before me. Some dreams begin before us; if we are fortunate enough, we get to carry them forward.

Academically, Queen Mary was the right fit. The emphasis on statistics, machine learning, decision-making, and ethics aligned with exactly the kind of AI career I wanted to build, one grounded in both technical depth and real-world responsibility. But what resonated most deeply was something harder to measure: Queen Mary is a place where ambition is the expectation, not the exception. That hit home for me. It’s the kind of environment that doesn’t just welcome people who want more, it assumes you do.

What did you most enjoy about your course?

The intellectual stretch. This programme does not let you coast, and I mean that as a compliment. Modules in statistics, risk and decision-making, and machine learning pushed me to understand the ‘why’ behind every method, not just the ‘how’. It was about assumptions, trade-offs, and limitations, the things that actually matter when you are building AI that affects real people.

The collaborative environment added another dimension, working alongside peers from diverse backgrounds made the learning feel genuinely reflective of how complex problems get solved in the real world.

Can you share an example of a project, assignment, or activity that stood out to you?

My dissertation. I built an Machine Learning pipeline to automate the classification of genetic variants, determining which DNA changes cause disease. It was trained on 2.95 million real clinical records and tested against the same standards clinicians use daily. 

What made it stand out was not just the technical work, but the weight of it. When AI is used in clinical settings, transparency is not optional, it is the difference between a tool clinicians can act on and one they cannot. That shaped how I think about building AI entirely. That project became GeneAId, a clinical AI startup I co-founded, now seeking its first accelerator and investor partnerships. The dissertation was the beginning.

Which modules did you enjoy the most and were there any academics that had a strong influence on shaping your time and studies here?

Statistics for AI and Data Science, Risk and Decision-Making, Machine Learning, and Information Retrieval. Each one deepened my mathematical intuition and sharpened how I reason about uncertainty, skills I use every day.

Two academics shaped my time here in ways that go beyond the classroom. My supervisor, Dr. Dimitrios Kollias, believed in my research direction and gave me the freedom and guidance to build something meaningful. Dr. Ahmed Sayed supported me through my first PhD application, a process entirely new to me. I received a PhD offer; the scholarship did not come through this time. But what Dr. Ahmed gave me was something more durable: an understanding of how the academic pathway works, and the confidence to keep walking it. That kind of guidance stays with you.

Describe your career path since graduating.

I was appointed AI & Data Innovation Senior Manager at Hikma Pharmaceuticals, an LSE-listed global pharmaceutical company (~$3.35B Revenue), where I lead AI and data-driven innovation in regulated environments, from governance frameworks to production machine learning systems. 

In parallel, I co-founded GeneAId, a clinical AI startup that grew directly from my MSc dissertation. We are pre-seed, building toward our first clinical partnerships and investor conversations. The degree did not just open a door, it gave me the foundation to build one.

How has your degree remained relevant throughout your career and are there any particular areas of your degree that you use in your day-to-day job?

Immediately and continuously. Statistical reasoning, decision frameworks, risk assessment, machine learning fundamentals, I draw on all of it, daily, across both my corporate role at Hikma and my work building GeneAId.

But the deepest gift was learning to think structurally, to break complex systems into assumptions, variables, and constraints before building anything. That is not a skill you use occasionally. It is the lens through which you see every problem.

What’s one piece of advice you’d offer to someone considering studying your programme at Queen Mary?

Come with curiosity, and stay with resilience. You do not need to know everything before you begin, but you must be willing to sit with difficulty, ask hard questions, and keep going when the material pushes back.

This degree is not just about algorithms. It is about learning to think clearly under uncertainty, build responsibly under pressure, and lead with both confidence and humility. The moments that stretch you most are the ones that define you. Lean into them.