Women in STEM – Maria Skoularidou

Recently, as part of its initiative for highlighting women in STEM, the University of Cambridge published an article about Maria Skoularidou – a PhD candidate in the MRC Biostatistics Unit. Maria is an expert in the emerging field of probabilistic machine learning and a keen advocate for Women in STEM and supporter for people with disabilities working in AI.

Maria’s research utilises elements from probabilistic modelling, a framework for representing and handling uncertainty about models and predictions, as well as machine learning, to develop a way of designing machines that learn from data acquired from experience. Such machines are likely to be particularly useful in healthcare applications, for those situations where data is sensitive from several aspects and where it is of great importance to be able to efficiently and accurately estimate uncertainty.

Maria recently said that being a researcher in AI meant that she had to efficiently manage her time. She has to keep abreast of the vast number of publications concerning AI appearing daily but, at the same time, has to find time to develop her own methods and interact with colleagues. She said now is “such a great time to be working in this field with so many open questions and plenty of room for applications that can improve people’s lives”. She also said her academic experience so far had enriched her with a toolset that was critical in problem-solving setups, such as algorithmic/methodological ways of thinking, programming in several languages, understanding quantitative and qualitative properties of problems and drawing inferences.

She also spoke of the pleasure she had had in meeting such living legends at Cambridge like Prof Sir David Spiegelhalter and Prof Neil Lawrence. Their commitment to science, their vision and enthusiasm had motivated and helped her a lot. When asked about what she thought about women considering a career in a STEM field, she said they should “go ahead and do it, and be assertive all the way through”. She also said there were numerous groups around that were capable of supporting women in STEM – she had recently been involved in a group called “Women in Data Science and Statistics”, a group supported by the Royal Statistical Society. [Look out for our page on the launch of the Women in OR and Analytics Network later in this issue – Ed.] 

She was also very proud of “{Dis}Ability in AI” which supports and advocates for people with disabilities in the field of artificial intelligence. It was, she said, officially supported by the top conferences in artificial intelligence worldwide.

More about {Dis}Ability in AI at: bit.ly/abilityAI
To attract more women into the STEM sector, gender stereotyping around science subjects must stop. Companies should feature female role models and offer a work/life balance. More about this at: bit.ly/STEMstereotype
The University of Cambridge website can be viewed
by accessing: www.cam.ac.uk
See also bit.ly/STEMbusiness
The Women in STEM website can be accessed at:
www.womeninstem.co.uk

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Now is “such a great time to be working in this field with so many open questions and plenty of room for applications that can improve people’s lives”