Tell us a little about yourself and your background
After receiving my PhD in 2008 in Electrical Engineering from MIT, I worked in industry as a big data quantitative modeller specialising in computational statistics on multimodal time series in Finance and Economic Development. In 2018, HDR UK gave me the opportunity to return to academia on the Rutherford Fellowship where I could realise my goal of conducting research in healthcare using artificial intelligence. That was my life’s turning point. There is nothing else I would rather do.
Tell us about your current role
My research focuses on the development of AI algorithms to predict disease progression for individualised early detection, diagnosis, and treatment-response stratification and safe-AI technologies to support responsible deployment.
What led you to enter health data science?
My dream has led me to health data science. My dream is to build and deploy innovations that can positively impact real-world healthcare outcomes. AI-for-health (AI4H) research has demonstrated human-level performance but has yet to impact routine clinical care. For real-world impact, AI4H must be (i) accepted by clinicians for use within their workflows, and (ii) be able to integrate within the hospital’s operating system with proper performance and risk management controls.
At UCL, I have been given the opportunity to work closely with clinicians throughout the life-cycle of innovation, from idea conception through to plans for deployment. The close collaboration enables us to co-develop AI solutions that optimise synergies between human and machine intelligence with embedded safe-AI techniques for future risk management design and implementation at institutional level.
What have been some of your proudest achievements?
To date, five of the algorithms in healthcare that we developed have been funded for patent-filing.
Do you enjoy working in academia?
One of the greatest gifts of being an academic is that you have a chance to mentor and help students gain the skills and confidence to make data science a tangible and rewarding career path on their own terms. There is nothing more encouraging than when your student comes back with new ideas that you can then learn from. It’s this positive feedback that I find most rewarding.
What’s the best piece of advice you have received and/or the one piece of advice would you give to other women entering your field?
A mentor once told me that the key to success is for one to always focus on his/her objectives, not on life’s circumstances. This was the best piece of advice I have ever received. What has really helped me stay focused on my objectives is to also work on my circumstances by finding the right people to work with. By working with like-minded people, who are aligned in vision and objectives, more than half the battle is won. Finding the right people is not always easy, though. It was a lot of work to try out different collaborations. But it was certainly worth the effort.
What one thing would you like to see happen to ensure that women can have flourishing careers in health data science?
I would like to see more equal opportunity hiring and for any existing gender-gap pay to be eliminated.