Notes

Thoughts, experiments, and lessons learned while building machine learning systems.

Why Interpretability Will Matter More Than Model Size

As models become larger and more powerful, understanding their decisions becomes increasingly important. Accuracy alone is rarely enough when systems affect real-world outcomes.

Lessons from Training Segmentation Models on Imperfect Data

Most datasets are messy. Real progress often comes not from architecture changes but from improving data quality, preprocessing, and evaluation methodology.

Reinforcement Learning Beyond Games

The most interesting applications of reinforcement learning are often found outside benchmark environments—in robotics, healthcare, optimization, and adaptive control systems.

The Future of Geospatial Intelligence

Satellite imagery, foundation models, and large-scale data processing are changing how we understand cities, infrastructure, and environmental systems.

Building AI Systems with Limited Compute

Constraints force creativity. Some of the most effective machine learning solutions come from careful engineering rather than larger hardware budgets.