Data Science Undergraduate · Sri Lanka 🇱🇰
>>> import amjad as me
>>> me.summary()
{
"role": "Data Science undergraduate @ NIBM",
"focus": ["statistical modeling", "machine learning", "data engineering"],
"currently_exploring": "AI agent architectures",
"org": "Design Team, IEEE Student Branch NIBM",
"status": "still fitting the curve..."
}
>>> me.dtype
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def solve(problem):
explore(problem.data) # never model what you haven't looked at
question(problem.anomalies) # noise is a clue, not an inconvenience
visualize(problem.findings) # a chart should argue a point, not decorate one
validate(problem.result) # p < 0.05 isn't the finish line, it's a checkpoint
return problem.result- Working through AI agent design & orchestration patterns
- Studied two-way ANOVA — hypothesis formulation, Correction Factor method, LSM in R & SAS
- Built a semantic network + knowledge representation model for a hospital domain (Draw.io + Python)
- Sketching event experiences with the IEEE SB NIBM Design Team
- Long-term: turning coursework into real, defensible data pipelines
💬 Open to collaborating on data science, machine learning, and AI agent projects