Maria Skoularidou is a PhD candidate in the MRC Biostatistics Unit. Here, she tells us about her work in the emerging field of probabilistic machine learning, meeting 'living legends', and her work supporting and advocating for people with disabilities working in AI.
Maria Skoularidou is a PhD candidate in the MRC Biostatistics Unit. Here, she tells us about her work in the emerging field of probabilistic machine learning, meeting 'living legends', and her work supporting and advocating for people with disabilities working in AI.
I am developing new methodologies in the field of probabilistic machine learning (PML), an emerging research area that lies at the intersection of statistics and computer science. PML uses elements from probabilistic modelling, a framework for representing and handling uncertainty about models and predictions; as well as machine learning, a way of designing machines that learn from data acquired through experience. In healthcare applications, where data are sensitive from several aspects, it is of great importance to be able to efficiently and accurately estimate uncertainty.
Being a researcher in artificial intelligence means that you have to efficiently manage your time. Since it is an emerging field, the rate of publications in AI is extraordinary, so I’ve got to spend time reading new things related to my topic. I also spend time developing my own methods, interacting with colleagues both inside and outside of the University, and coding. All four aspects are very important. I’ve also discussed my project with people from other universities and research institutions across the world and that has been very motivating in its own right.
It’s such a great time to be working in this field. There are so many open questions and plenty of room for applications that can improve people’s lives. My academic experience so far has enriched my arsenal with tools that are critical in problem-solving setups, such as algorithmic/methodological way of thinking, programming in several languages, understanding quantitative and qualitative properties of problems and drawing inference, which I hope to use to answer challenging questions in the broader field of probabilistic machine learning and artificial intelligence.
I’ve met some living legends at Cambridge, like Professor Sir David Spiegelhalter and Professor Neil Lawrence. Their commitment to science, their vision and enthusiasm have motivated and helped me a lot. As a Cambridge PhD student, you can also apply for ‘internal’ scholarships and, if successful, get the opportunity to further your research in other universities.
My advice for women considering a career in a STEM field is to go ahead and do it, and be assertive all the way through! There are a lot of groups out there to support you in this regard. We have recently created the Women in Data Science and Statistics group, with the generous support of the Royal Statistical Society. This group, among others, advocates for women in statistics and data science and supports them in these fields.
Another group I am very proud of is {Dis}Ability in AI, which supports and advocates for people with disabilities in the field of AI. It is officially supported by the top conferences in artificial intelligence worldwide (NeurIPS, ICML and ICLR).
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