About me

I am PhD student at Université de Montréal/Mila working on applications of machine learning for biodiversity conservation and to tackle climate change under the supervision of Prof. Yoshua Bengio,Prof. Hugo Larochelle and Prof. David Rolnick. My current research focuses on biodiversity monitoring using remote sensing data and citizen science data. I am particularly interested in species distribution modeling and forest monitoring. With a wonderful team, I also worked on ThisClimateDoesNotExist.com, a website to visualise the impacts of climate change in our own backyard. I am also happy to be contributing 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).