Projects & applied work
Selected work across data analysis, graph-based research, Machine Learning foundations, curriculum development and interactive learning.
Selected portfolio
Projects include completed analyses, structured educational resources and research pipelines under active development.
A reproducible next-day PM2.5 forecasting study for Lahore using OpenAQ observations, NASA POWER weather and honest time-series validation.
A reproducible Python case study examining data quality, transaction distributions, daily sales performance and customer contribution with statistical interpretation.
A research pipeline exploring metric coordinates from resolving sets as interpretable structural node features for neural networks and graph neural networks.
A curriculum model connecting mathematics and statistics with Python, analytics, applied projects, internships and emerging computational applications.
Browser-based and Colab-ready experiments that let learners manipulate mathematical ideas, run code and interpret results rather than read passively.
Project principles
Projects are selected for learning value, mathematical relevance and transparent evidence—not simply for technical display.
What problem is being examined?
Why is this method appropriate?
Can the process be followed and tested?
What does the result actually mean?
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