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BCSWomen organises the annual Lovelace Colloquium: a day featuring talks, a careers panel, employer stands and a student poster contest. This year, three students from the University of Edinburgh made the trip to Sheffield, and Qiuye Zhang in fact won first place with her poster “Can Artificial Neural Networks Learn like Brains?” in the second year contest! Here is how she experienced the event:
I am excited to share my experiences and insights from the Lovelace Colloquium, where I had the opportunity to present my poster on computational neuroscience and computational psychiatry. It was my first time discussing these two fascinating fields publicly, and I was thrilled to see some attendees express interest in computational psychiatry.
Initially, my abstract didn’t mention computational psychiatry, but after being inspired by Peggy and her course on computational cognitive neuroscience, I decided to include it in my poster. The interest and discussions surrounding my presentation exceeded my expectations. We delved into topics beyond the scope of my poster, such as Hopfield networks, Bayesian models, and reinforcement learning models. The judges of the contest were very encouraging about my current research. Their kind words and support reinforced my passion for the subject and motivated me to continue my work in this field.
The event also allowed me to meet many amazing people who provided warm hugs and support when I felt nervous before my presentation. In addition to my poster experience, the keynote speeches were enlightening. They touched on the biases faced by females, gender-neutral individuals, and disabled people, as well as the use of technology to detect violence.
Going forward, I plan to be more mindful of potential biases in my research, particularly concerning people with psychiatric diseases. I will consider whether they receive adequate support and explore how to facilitate their lives when cognitive control is a challenge.
Overall, the Lovelace Colloquium was an enriching experience that allowed me to share my passion for computational neuroscience and psychiatry, learn from others, and connect with amazing people. I’m grateful for the opportunity and look forward to applying my newfound insights in my future work.
Qiuye Zhang’s poster “Can artificial neural networks learn like brains?”
There are disproportionately few women enrolling for undergraduate degrees in computing in the UK. Despite constituting 50.5% of the UK population and 57% of college graduates in the UK, only 19% of the technology workforce are women. The statistics for staff within our school align well with the national figures, with women constituting 56% of our professional services staff but only 20% of our academic staff. The disparity is lower in our student population: 27% of taught students (24.2% of undergraduates and 36% of post-graduate) and 22% of research students identified as female.
The decisions made by pupils in the last two years of high school is a key contributor to this disparity. The female to male ratio for first year STEM undergraduates across the UK hovers around 1, but its breakdown across disciplines reveals wide variation across the sciences (Figure 2 shows a detailed breakdown for interested readers). About one in four Computer Science (or Engineering) undergraduates identifies as female. Those who identified as ‘other’ when given a 3-way choice of gender (about 0.4% of UK’s population) make up about 0.2% of first year undergraduates in the UK; within our school the numbers are significantly better than the national average (0.7% of taught students and 2% of research students).
Most of the interventions designed and delivered in the UK [2,3], to reduce gender disparity in STEM, have been targeted at high school students. Specifically, focussing on female pupils to educate them about the benefits of choosing careers in science, via mechanisms such as the Stimulating Physics Network [1]. It appears that female pupils choose non-mandatory STEM subjects [9,10] in secondary schools when they:
believe that they are `good’ at it;
appreciate the value of science;
are embedded in a micro-culture that values and discusses science [7]; and
are exposed to role models provided they do not conform to STEM stereotypes [4,5].
The UK has spearheaded studies related to 3, under the umbrella of ‘science capital’ [6].
Paradoxically, there is evidence [8] that gender disparity in engineering and technology is inversely related to national gender equality. That is, countries with higher percentages of women engineers (around 40%) tend to have a poor global gender gap index. E.g. Algeria, Tunisia, U.A.E., Turkey, Indonesia, Vietnam.
In summary, with only 20-25% of undergraduates in Engineering and Computer Science being women, the status quo precludes the majority of women in the UK from gaining the skills needed for lucrative tech jobs (Figure 1). It is a large and complex issue. What can we at the School of Informatics, as the powerhouse of computer science in the UK, do about this?
My view is that we could aim to overcome this disparity at three different levels:
we are probably large enough to effect change by leading and organising effort at the national level (spawning something like the SPN but specifically for computing) while liaising with government (e.g. Scottish Parliament, Department for Education);
locally, we could identify appropriate [5] role models within the school who connect with schools to keep female pupils (and their teachers) informed of the impact of their choice of subjects to society and their own careers; and finally. For example through the Informatics Tutoring Scheme.
every one of us should actively contribute to an inclusive environment within the school. Although this sounds obvious and trivial, we continue to hear about scope for improvement, in this regard, via our student surveys.
What do you think?
Fig 2. Ratios of full-time female to male students in first year undergraduate (left) vs graduate (right) programmes in the year 2020-2021. Data from HESA.
