I am a first-year PhD student, focusing on the sustainability of machine learning with a particular emphasis on data efficiency. My work examines how the scale and use of data shape the environmental and societal costs of AI, and how more data-frugal approaches can support responsible development. I am especially interested in how decisions around data collection, curation, and usage influence energy consumption, carbon emissions, and downstream impacts.
My recent work includes How Hyper-Datafication Impacts the Sustainability Costs in Frontier AI and Stop Preaching and Start Practising Data Frugality for Responsible Development of AI, which explore the consequences of data-intensive practices and advocate for more efficient alternatives.
I hold an MSc in Computational Physics from the Niels Bohr Institute, where I studied physics-informed machine learning as an approach to reducing the energy consumption and carbon footprint of ML models.
Alongside my research, I am engaged in outreach activities and am committed to promoting greater participation of women in STEM, particularly by inspiring younger audiences.