[MENTEE] Seyi Kuforiji #75
Description
Mentee:
I am an AI Engineer and active open-source contributor. I was recognized for my contributions to the Git project, where I modernized its internal unit testing infrastructure spanning thousands of lines of C code. My contribution was featured in the official Git 2.49 release article by GitHub.
Beyond systems engineering, I hold a Stanford certification in Health Data Science, Precision Medicine, and Cloud Computing. My interests include machine learning, reproducible research, model evaluation, and building reliable data-intensive systems. I have experience working with Python, Spark, SQL Server, and distributed data pipelines, and I care deeply about writing maintainable, well-tested scientific software.
| 🧑💻 GitHub | @Seyi007 |
| 💼 LinkedIn | linkedin.com/in/seyi-kuforiji-3a591b130/
| 📫 Portfolio | seyi-kuforiji.xyz
Mentor(s):
Why did you join sktime's mentorship program?
I joined sktime’s mentorship program because it combines research-driven machine learning with strong software engineering practices. I am particularly interested in time-series modeling, evaluation frameworks, and building reliable ML infrastructure.
With my background in both large-scale systems engineering and data science, I’m excited to contribute to a library that emphasizes reproducibility, API design, and rigorous experimentation.
What topics are you working on?
I am especially interested in:
- Testing and validation of estimators
- Model evaluation pipelines and benchmarking
- API consistency and contributor tooling
- Reproducibility and performance optimization
(I will link specific issues/PRs as I begin contributing.)
What are your learning goals?
- Deepen my understanding of time-series forecasting and evaluation
- Strengthen my knowledge of scientific Python ecosystem design
- Learn best practices for research-oriented open-source ML projects
- Contribute high-quality, reproducible ML tooling
What's next for you after the mentorship program?
I intend to continue contributing to sktime long-term. I have applied for structured programs for ESOC 2026 to begin this journey, as I hope to expand my work in open-source machine learning and AI systems.