We interview leading women in STEM to learn more about how we can all work to make science and technology industries more inclusive. How can more women be encouraged to work in these fields?
Silvia Chiappa is a Senior Research Scientist at DeepMind, working at the intersection of probabilistic modeling and deep learning. Prior to DeepMind, she worked at Microsoft Research Cambridge, at the Statistical Laboratory University of Cambridge and the Max-Planck Institute for Biological Cybernetics.
I spoke with Silvia to learn about her career in science, how we can overcome barriers for women in tech, and more.
How did you begin your work in science and technology?
At the age of 12 I started to appreciate the elegance of maths when learning about trigonometry. I found a great sense of satisfaction in solving challenging trigonometric puzzles and was fascinated by the fact that things would always add up! I nevertheless continued my studies at a humanistic high school, as this was considered the most prestigious school in my home town. There, the maths teacher was engaging with me much more than with the other students, as they were not showing much interest in the subject. She also decided to train me for the selection of the Italian Mathematical Olympiad.
Thanks to her, I became more and more curious about maths and decided to study it at university. This was the best decision I could possibly take, as maths is an extremely interesting and beautiful discipline, and it opens the door to many different jobs.
Which emerging or future technologies are you excited about?
I am most excited to see how machine learning and AI will enable us to make progress in the medical domains.
What do you think the biggest barriers are for women entering and staying in the industry? How can we overcome these?
Current workplace policies and culture are lacking support for work-life balance. This makes it extremely difficult for women that need to take care of the family, especially to reach more senior positions.
Concrete steps that we can take to change the current situation are on-site childcare, access to flexible or part-time working, tailored career paths that account for family commitments, and changing the culture that the ideal worker is one who is "committed to their work above all else".
Do you have any advice for someone starting a career in your field?
I would suggest to develop a solid background in machine learning, through learning about the main disciplines underlying it, namely linear algebra, probabilistic reasoning, statistics, and optimization. A solid background makes it easy to understand recent AI advances and make contributions. A big mistake would be, for example, to study deep learning without developing such a background, as this would give a very myopic view about it.
It is also extremely important to develop a feeling of how algorithms work in practice, thus I would suggest to work on many practical diverse projects.
Finally, I would suggest to build knowledge about different areas of machine learning through the distilled information provided by leading figures in blog posts and other internet sources -- this is a shortcut to build knowledge that would otherwise take a long time to develop.
See our Women in Tech & Science series for more Q&As.
Are you working in emerging areas of science and technology, or know of someone who is? Suggest women in STEM fields to speak at a RE•WORK event here.
Confirmed speakers include Sangram Ganguly, Senior Research Scientist, NASA & BAER Institute; Andrew Tulloch, Research Engineer, Facebook; Sanja Fidler, Assistant Professor, University of Toronto; Charlie Tang, Research Scientist, Apple; and Dilip Krishnan, Research Scientist, Google. View more details here.
Opinions expressed in this interview may not represent the views of RE•WORK. As a result some opinions may even go against the views of RE•WORK but are posted in order to encourage debate and well-rounded knowledge sharing, and to allow alternate views to be presented to our community.