Top 100 Women in Engineering: Amber Hill

I joined the programme wanting to combine all of my technical know-how and domain knowledge to create a solution that could open new possibilities and markets for innovation in my specialist areas of research and medicine. In particular, I wanted to lead a company that would utilise machine learning to have a greater impact than what I was previously able to have manually via any other approach.

I’ve always been the kind of person to challenge myself with multiple facets and moving parts at once. It used to drive my mother and any supervisor I’ve ever had a bit nuts! But, it’s exactly what the CEO role is. For me, entrepreneurship is akin to a longitudinal research experiment where you are applying the scientific method to test a hypothesis to learn faster with data-driven output than your competitors.

While I’ve been studying or working in medical research my entire life, I had a very unconventional path to software engineering via neuroscience. Ahead of my PhD at UCL, which heavily involved physics, I had never taken a physics class, ever. Now, I’m one of the few in the world who can run a 9.4T Magnetic Resonance Scanner, or single-handedly do my own behavioural, cellular, histology, animal, clinical, or metadata-based research. Likewise, ahead of deciding that I would streamline elements of my research and work into AI/ML software, I had never taken an engineering or software course. In fact, I was told by a superior that I wouldn’t be able to do it (and I’m just not the sort of person that you say that to!). I self-taught, went to Harvard to learn more, shifted to engineering as a Royal Academy of Engineering Enterprise Fellow, and now I’m the CEO of a ML/AI start-up. Entrepreneur First has helped to focus and accelerate every goal I’ve set this past 6 months! I suppose the moral of my story is that you can dive into spaces that you’ve never been exposed to, and excel at them if that’s what you decide you want. I apply a deeply scientific approach and perhaps pure grit to this theory (along with a tattoo that says “decide” for good measure).

R.grid is perfectly placed and emerging at the right time because of the cultural shift in medical research to digital. We now have security and data regulations like GDPR and a human-centric model like PPIE (patient and public involvement and engagement) set by funders and regulators to drive research into prioritised areas. What’s missing is the translation of technological capabilities to real root issues in the research process that contribute to the 86% of failures that happen in clinical research.

R.grid is set to reduce the 2+ years of admin (lead time, reporting, etc) per clinical study to seconds or minutes, by streamlining manual research processes with machine learning. R.grid also reduces admin costs by a minimum of 30%, and increases patient involvement in studies by 10 fold. I’m so excited for what’s to come after we raise our first £1M.