Muhtasim Munif Fahim

Neural scaling laws describe how language model loss decreases with parameters and data, but treat architecture as interchangeable. We propose architecture-conditioned scaling laws decomposing depth-width dependence, finding that optimal depth scales as D* ~ C^0.12 while optimal width scales as W* ~ C^0.34, meaning width should grow 2.8× faster than depth. We discover a critical depth phenomenon: beyond D_crit ~ W^0.44 (sublinear in W), adding layers increases loss despite adding parameters—the Depth Delusion. Validated across 30 transformer architectures spanning 17M to 7B parameters (R² = 0.922), our central finding is that at 7B scale a 64-layer model (6.38B params) underperforms a 32-layer model (6.86B params) by 0.12 nats, despite being significantly deeper—demonstrating that optimal depth-width tradeoffs persist at production scale.

We introduce Green-NAS, a multi-objective neural architecture search (NAS) framework designed for low-resource environments using weather forecasting as a case study. Adhering to Green AI principles, the framework explicitly minimizes computational energy costs and carbon footprints, prioritizing sustainable deployment over raw computational scale. The search simultaneously optimizes model accuracy and efficiency to find lightweight architectures with very few parameters. Our best-performing model, Green-NAS-A, achieved an RMSE of 0.0988 (within 1.4% of a manually tuned baseline) using only 153k parameters—239 times fewer than globally deployed models such as GraphCast. Transfer learning improves forecasting accuracy by approximately 5.2% compared to training a new model per city when historical data is limited.

A large number of children experience preventable developmental delays each year, yet deployment of machine learning in new countries is stymied by a data bottleneck: reliable models require thousands of samples, while new programs begin with fewer than 100. We introduce the first pre-trained encoder for global child development, trained on 357,709 children across 44 countries using UNICEF survey data. With only 50 training samples, the pre-trained encoder achieves an average AUC of 0.65 (95% CI: 0.56–0.72), outperforming cold-start gradient boosting by 8–12% across regions. At N = 500, the encoder achieves AUC of 0.73. Zero-shot deployment to unseen countries achieves AUCs up to 0.84. We apply a transfer learning bound to explain why pre-training diversity enables few-shot generalization, establishing that pre-trained encoders can transform the feasibility of ML for SDG 4.2.1 monitoring in resource-constrained settings.