Jeanne is a first year PhD student in the chair of condensed matter theory lead by Professor Frédéric Mila at École polytechnique fédérale de Lausanne, in Switzerland. The general aim of the group is to explore new phases of matter induced by strong correlations in electronic systems, which is done by investigating analytically and numerically the role of frustration or competing interactions in lattice models of low-dimensional quantum magnetism. She is the recipient of one of the poster prizes sponsored by Nature Reviews Physics at the Machine Learning for Quantum Many-body Physics workshop that happen last June in Dresden.
1. Can you briefly explain the results for which you got the award?
I have been mainly focusing on the Ising model with antiferromagnetic further-neighbour couplings on the kagomé lattice. I am doing Monte Carlo simulations to try and understand how the physics of this model changes depending on the range of the interactions taken into account. It was a nice surprise to get an award for my poster, given that the main focus of the conference was Machine Learning for quantum many-body physics, and I have not been doing machine learning so far.
2. What do you hope will be the impact of your research?
I am looking at a very specific problem, so I think the dream would be that new, more general questions would arise from studying this system.
3. What made you want to be a physicist in the first place?
For me, it is a good balance between trying to understand our surroundings, trying to solve interesting and challenging problems, and meeting dedicated people whom I have a lot to learn from.
4. If you weren’t a physicist, what would you like to be (and why)?
I think, as long as I would be trying to solve some problems and would feel useful in some way, I would be happy.
5. What would be your physics superpower?
Asking the right question right away.
6. What is your non-scientifically accurate guilty pleasure?
Maybe I don’t feel guilty enough about it, but I spend a lot of time playing and listening to music.