About me

I am a postdoctoral researcher at the Pioneer Centre for AI, University of Copenhagen, working on applications of machine learning for biodiversity conservation, in particular, species distribution modelling, forest monitoring and bioacoustics. I completed my PhD at Université de Montréal/Mila, advised by Prof. Yoshua Bengio, Prof. Hugo Larochelle and Prof. David Rolnick. With a wonderful team, I also worked on ThisClimateDoesNotExist.com, a website to visualise the impacts of climate change in our own backyard. During my PhD, I was a student researcher at Google DeepMind in the Perch team, and contributed to Future Earth Canada’s initiatives, including work on Nature-based climate solutions with Indigenous Climate Action and with the Coalition for Digital Environmental Sustainability.

I earned a Master’s degree in Applied Mathematics, Computer Vision and Machine Learning (MVA) from Ecole Normale Supérieure Paris-Saclay, and completed a double degree (MSc in Engineering and Applied Mathematics / MSc in Management and Social Entrepreneurship) at Ecole Centrale Paris and ESSEC Business School.

I was a 2018 Data Science for Social Good fellow where I worked with the Croatian Institute of Public Health on proactive advising for vaccine outreach, and a 2019 Voting Rights Data Institute fellow.

Beyond research

I generally love learning new things and sharing what I have learned, and it is important to me to give back to the community! I was Teaching Fellow for the amazing initiative Delta Analytics and a mentor for Technovation. At Mila, I am a member of the Equity, Diversity and Inclusion Committee and a member of the Sustainability Committee, and was a Lab Representative in 2021.

I find a lot of joy in writing stories, mostly in the form of comics recently, if I’m not at the metalsmithing or pottery studio. I enjoy painting, hiking, playing the piano, reading and spending time with my friends, … I sing in the Newman Chapel Choir and I played the bass drums in the Princeton University Band which was really fun (scrambling > marching).