Distributed LLM pretraining during renewable curtailment windows 🌱

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.

End-to-end AI deployment decision system combining real model benchmarking, system-level optimization, and infrastructure-aware trade-off analysis (latency, cost, energy, carbon).

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

Blackout Markets is a shadow optimizer for AI infrastructure teams. It recommends when GPU workloads should run, wait, or move regions based on energy cost, carbon intensity, GPU capacity, latency, reliability, and policy constraints.

EcoLogic is a local Streamlit toolkit for generating and evaluating algorithmic refactors across single files or full codebases. It predicts energy use with feature-based ML, profiles Python/C++/.NET/Java workloads, delivers optimized code with SHAP-powered explainability, and creates shareable PDF certificates for auditable, interpretable results.

Pre-alpha carbon-aware DevOps / CI/CD reference toolkit.

Carbon-aware cloud workload scheduler that reduces emissions by intelligently shifting workloads across time using multi-objective optimization.

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