Jillian Ross is a PhD student whose research interests focus on the climate impacts of machine learning. Her goals include reducing ML’s carbon intensity, enabling researchers and practitioners to understand the climate impact of various tools, and guiding future research in efficient ML. Jillian’s achievements to date include leading community and growth at Replicate, a Series A start- up enabling software engineers to run ML models with only a few lines of code, and co-producing a free, online resource “Ethical Computing Platform,” drawing on her combined strengths in machine learning and philosophy. With the support of a MathWorks Fellowship, Jillian plans to release the first open dataset mapping energy usage to model metadata, along with accompanying data analysis and visualizations and an interactive website. Additionally, she will use the dataset to map the efficiency of ML techniques to inform current and future decision-making by researchers and practitioners. MathWorks is a key component in Jillian’s work, which holds exciting potential to advance ML applications that balance accuracy, speed, and climate-friendly efficiency, as this important field continues to grow.