_{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
'perpetually learning'

df.stack() — the tools in my pipeline

layer	tools

for step in approach:

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

git log --author=amjad --since="recently"

• 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

plt.show() — the numbers behind the numbers

me.contact()

_{💬 Open to collaborating on data science, machine learning, and AI agent projects}