Learn to optimize machine learning tasks for environmental sustainability. Discover how to use real-time electricity data and low-carbon energy sources for model training and inference, reducing the carbon footprint of your cloud operations.

Hackathon winner at AI Engineer World Fair Hackathon: Transforming code, one function at a time, to reduce digital carbon footprints and create a more sustainable digital world.

A lightweight pipeline for carbon-aware job scheduling using 72-hour forecasts of grid renewable energy share.

A thesis implementing and evaluating a framework for energy-aware federated learning, capable of both simulation and real-time monitoring.