Sanae was born in Morocco and attended CentraleSupélec in France after completing a two-year intensive program in science to prepare for the entrance exam to France’s top engineering schools.
She followed her degree in general engineering and applied mathematics with her Masters at Polytechnique Montréal, and is now pursuing a PhD at the Center for Data Science (CDS) at New York University, investigating deep learning models. Sanae received several awards for academic excellence, including the French government excellence scholarship, McKinsey’s first-generation achievement award, and more recently, the best Masters thesis award in applied mathematics at Polytechnique Montréal for 2020.
I believe one of the biggest obstacles that people from underrepresented groups face is a lack of information. So the advice I would give is to harness every single resource you have access to, and to aim for excellence in your work. This can help you build your own vision and reduce your fears.”
In conversation with Sanae
Were you familiar with DeepMind and the scholarship programme before your PhD?
I was familiar with DeepMind – I’ve read several papers published by DeepMind researchers and was fortunate to meet people from the team during the NeurIPS 2018 and ICLR 2019 conferences. I’d also heard of the DeepMind scholarship before receiving it, but I didn’t have any idea about the nomination process.
How did you feel when you found out that you were chosen to be a DeepMind scholar?
I felt very happy and honored to be a DeepMind scholar. It gave me immediately a sense of belonging to the machine learning field and it meant that my work and dedication had been seen and appreciated by others. I think it’s very encouraging for a first-year PhD student to start their program with such generous support and recognition from a leading company. I will always be grateful to DeepMind for that.
What inspired you to pursue a PhD at NYU?
I decided to pursue a PhD after two six-month R&D internships during my studies at CentraleSupélec. Thanks to these internships, I realised that I wanted to spend my career working deeply and thinking creatively about complex problems to which there are no obvious answers.
During my Masters studies, I discovered that research can be very demanding, and that some problems might not have an answer. However, I still felt satisfied working on them and passionate about the next idea or research paper that might lead me somewhere or change my perspective of the problem. So, applying for a PhD was the natural next step. NYU's CDS (Center of Data Science) has a very dynamic and multidisciplinary environment that offers the possibility of interacting daily with people working on a wide range of theoretical problems and applications in different branches of data science. Also, New York City is home to many top AI companies and researchers, and it’s an exciting place to be while pursuing a degree in data science.
What was having a mentor like?
The scholarship comes with the chance to be mentored by a DeepMind researcher. This has been a great opportunity for me to get to know the company more and understand the difference, in general, between research in industry and in academia. My exchanges with my mentor have been very informative, helpful, empowering, and inspiring. I am very thankful for this opportunity and I look forward to sharing everything I learned with other people.
If you could accomplish one thing using your degree, what would it be?
My aim is to gain in-depth knowledge and expertise in the sub-fields of machine learning that I am interested in. This would give me the chance to contribute to those fields and put me in a position where I can help alleviate barriers preventing people from underrepresented groups from pursuing careers in STEM.
What is one piece of advice you would give another young person from a group underrepresented in AI / ML who is interested in - but apprehensive about - pursuing a degree in your field?
I believe one of the biggest obstacles that face people from underrepresented groups is the lack of information. So the advice I would give is to harness every single resource you have access to and to aim for excellence in your work. That would include interacting with your university professors, attending workshops and conferences - especially now that many of them are held online - and reaching out to people that work in the sub-field you are interested in.