About

Curious by default. Rigorous on purpose.

I’m an applied mathematician who found a home in data science because it rewards both abstraction and usefulness.

Hello, I’m Shakil

I like the space between a hard idea and a clear explanation.

My work sits across generative AI, machine learning, causal inference, computational biology, optimization, and education. The thread connecting them is translation: from messy data to defensible evidence, from mathematical structure to working software, and from expert language to shared understanding.

Today, I’m a Teaching Assistant Professor of Data Science at the University of Arkansas. I work with students and industry partners on modern modeling, privacy-aware data systems, RAG and agentic workflows, and the practical craft of deploying analysis responsibly.

Before that, I developed generative and interpretable methods for genomic data, worked on maternal-health analytics, and spent years teaching mathematics from precalculus through differential equations. I still believe the best data scientists are part mathematician, part engineer, part skeptic, and part storyteller.

Outside the work, I enjoy hiking, the outdoors, and maintaining a long-running status as an aspiring juggler.

Principles

What I bring to the table.

01

Mathematical depth

I care about assumptions, structure, and why a method works—not only whether a library returns an answer.

02

Applied judgment

The “best” model depends on privacy, cost, maintenance, explanation, and the actual decision it supports.

03

Teaching clarity

If an idea cannot be explained honestly, the implementation probably deserves another look.

Technical range

The tools change. The thinking travels.

I choose technology to fit the system and the team. These are the tools I reach for most often.

PythonRSQLRustC/C++JuliaMATLABPyTorchTensorFlowKerastidyversetidymodelsLangChainLangSmithOllamaFAISSMLflowDockerNextflowShinyStreamlitPyomoCVXPYPySALGitLinuxHPCSlurm
Selected speaking

Ideas are better when they travel.

ASM Microbe 2025Deep learning and pathways of pathogenicity in E. cecorumLos Angeles, California
SIAM UQ 2024Maximal parameter estimates for neural networksTrieste, Italy
HDSI 2023Summer graduate school on machine learningUC San Diego
SIAM MDS 2022Clustering rideshares during the pandemicSan Diego, California
Say hello

Good questions are welcome here.

Research, teaching, responsible AI, open source, or a problem that refuses to fit neatly in one box—I’m glad to talk.

Email me LinkedInGitHub