Sasha Luccioni
AI & Climate Lead
Montreal, Quebec, Canada
Summary
🤩
Rockstar🎓
Top SchoolSasha Luccioni is a research scientist and AI practitioner based in Montreal who bridges cutting-edge machine learning with climate and social impact work across academia, industry, and policy. With a PhD and postdoctoral training, she has eight years of experience working at institutions like Mila, McGill, Hugging Face (AI & Climate Lead), and founding roles in Climate Change AI and the Sustainable AI Group. Her contributions span research, teaching, and practical tooling—she’s improved core ML evaluation metrics in the popular Hugging Face evaluate library and helped make CodeCarbon’s emissions-tracking docs clearer and more accessible. Sasha combines rigorous publication-grade research with hands-on open-source engineering and policy engagement (OECD.AI, UNESCO), making her fluent in both technical development and ethical/environmental governance. Colleagues value her ability to translate complex ML topics into practical guidance for reducing compute emissions while preserving model performance.
8 years of coding experience
11 years of employment as a software developer
Bachelor of Arts (B.A.), Bachelor of Arts (B.A.) at Université Sorbonne Nouvelle (Paris III)Doctor of Philosophy - PhD, Doctor of Philosophy - PhD at Université du Québec à MontréalMaster of Science (MSc), Master of Science (MSc) at Ecole normale supérieurePostdoctoral Degree, Postdoctoral Degree at Université de MontréalBachelor's degree, Bachelor's degree at Ewha Womans University
Russian, French, Spanish, Portuguese, English
Github Skills (8)
tokenize10
machine-learning10
tokenizer10
nlp10
bleu10
python10
sphinx10
documentation10
Programming languages (9)
Github contributions (5)
huggingface/evaluate
Mar 2022 - Dec 2022
🤗 Evaluate: A library for easily evaluating machine learning models and datasets.
Role in this project:
ML Engineer
ML EngineerContributions:81 reviews, 187 commits, 44 PRs in 9 months
Contributions summary:Sasha primarily contributed to the `evaluate` library by enhancing the BLEU metric. They implemented a tokenizer function and updated the README. The user also worked on standardizing tokenization approaches by integrating `tokenizer_13a` from SacreBLEU and adapting it for use within the `evaluate` library. Furthermore, the user updated the Google BLEU implementation and added examples.
evaluateevaluation-frameworkdata-scienceevaluationmachine-learning
mlco2/codecarbon
Aug 2020 - Dec 2022
Track emissions from Compute and recommend ways to reduce their impact on the environment.
Role in this project:Technical Writer
Technical WriterContributions:3 reviews, 26 commits, 6 PRs in 2 years 3 months
Contributions summary:Sasha's contributions primarily focus on updating and expanding the project's documentation. This includes revisions to existing documentation files such as `methodology.html`, `edit/methodology.rst`, `edit/usage.rst`, `edit/parameters.rst`, `edit/motivation.rst`, `edit/model_examples.rst`, and `faq.rst`, suggesting a thorough review and enhancement of the project's documentation. The user also added a new `comet.html` page, integrating Comet.ml, as well as adding an FAQ page. These commits improve the clarity and usability of the project for other developers.
pythondata-scienceemissionscarbon-emissionsrecommend
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.