Peer-reviewed publications

Green AI & Sustainable Machine Learning Research

Published as Muhammad Ahmad Zia — researcher and lecturer at the University of Lahore. My work investigates how machine learning systems can deliver equal or better results at significantly lower compute, energy, and cost, with applications across sustainable computing, smart cities, and commercial AI workloads. Many projects have open-source code available on GitHub.

🎓 Google Scholar 📑 Scopus 🔬 ResearchGate 🆔 ORCID 💻 GitHub

Health-Aware AI

Is 'Suitable for Sensitive Skin' Actually Safe? We Tested 1,263 Skincare Products to Find Out

Future Cities Conference 2026 (FCC'26), Healthy Cities Track

A new in-press study adds a transparent, regulator-grounded safety layer to skincare recommenders, tested on 1,263 real products and a live Pakistani marketplace. The finding: today's suitability labels barely protect shoppers past the first page of results, but a re-ranker built entirely from public regulator lists cuts allergen exposure by up to 99 percent at almost no cost to recommendation quality, and every flag it raises traces back to a named public source.

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Green AI

Does Lowering Precision Actually Speed Up AI Inference? We Ran 6,000 Benchmarks to Find Out

In Press

A new in-press study benchmarks gradient-boosted tree inference across four free-tier cloud platforms, three engines, and five precision levels using 6,000 measured runs. The surprising result: shrinking floating-point precision barely helps, naive INT8 quantization can quietly wreck accuracy, and engine choice — not hardware — explains nearly 80% of the speed difference.

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Research That Feeds Real Client Work

Every finding above informs how I build for clients — and every client deployment feeds evidence back into the research.

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