Teach the foundations. Then build the frontier.
I help students become data scientists who can reason before they reach for a library, validate before they celebrate, and explain what their work means.
Technical ambition with an ethical center.
Students should leave a course able to build—but also able to ask whether a system should be built, how it can fail, and who carries the consequences.
Reason before recipes
Models become durable knowledge when students understand assumptions, geometry, uncertainty, and tradeoffs—not just function calls.
Build the whole path
Git, environments, cleaning, modeling, testing, deployment, monitoring, and communication belong in the same learning journey.
Make failure visible
Students examine privacy, leakage, bias, hallucination, and evaluation gaps as engineering realities—not footnotes.
From p-values to production-minded AI.
Where confidence becomes competence.
I meet learners where they are, then make the next step visible. The standard stays high; the path becomes clearer.
First-year data-science students
Taught Python, R, Git, Bash, statistics, and reproducible analytical habits.
Advanced optimization students
Mentored applied work across routing, network flow, and facility-location problems.
Course design recognition
Inaugural recipient of the University of Arkansas Global Campus Award for Excellence in Course Design.
STEM pipeline teaching
Contribute to early-access data science, Python, and AI instruction through University of Arkansas programs.
“Students do not need AI to make thinking optional. They need it to make better thinking possible.”Teaching principle · Shakil A. Rafi
Learning is a team sport.
Interested in a guest lecture, applied project, curriculum collaboration, or industry partnership?