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My name is Chufeng Jiang (蒋楚枫), the pronunciation is [tʃu:fɛŋ]-[dʒɑːŋ]. You may just call me “Beza“, the pronunciation is [beɪ za], an Ethiopian name given by my colleagues meaning “good luck” and “the gift from the God“. I love this name so much because I am always on a lucky streak!

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I am a 1st year CS PhD student at The City University of New York, where I am advised by Dr. Raffi Khatchadourian. My research interests lie in:
  • Technical Debt, currently focused on data debt in ML Systems;
  • Software Engineering and Programming Language;
  • Machine Learning System.

I am particularly focused on applying Software Engineering methodologies to address Technical Debt in Machine Learning Systems. This involves mining code and data across diverse models to identify refactorings that specifically target ML-related technical debt. Additionally, I aim to develop innovative refactoring techniques at both the code and data levels, integrating object-oriented practices to simplify and enhance the maintainability of complex, interdependent models.

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What is Technical Debt?

Technical debt refers to the long-term costs incurred by taking shortcuts during implementation and deployment. These costs can hinder maintenance, scalability, and improvements over time. In ML Systems, unchecked technical debt can degrade model performance, increase operational costs, and stifle long-term innovation [1-4].

One of our group’s preliminary studies is “An Empirical Study of Refactorings and Technical Debt in Machine Learning Systems” [3], which might give you some insight into our research topic.  (Clicking on the image will take you to the original article).

https://ieeexplore.ieee.org/document/9401990

Fig 1: Discovered refactorings (hierarchical).[3]

References

[1] Sculley, David (2021). “Technical Debt in ML: A Data-Centric View”. In: Advances in Neural Information Processing Systems.

[2] Sculley, David, Gary Holt, Daniel Golovin, Eugene Davydov, et al. (2015). “Hidden Technical Debt in Machine Learning Systems”. In: Advances in Neural Information Processing Systems.

[3] Tang, Yiming, Raffi Khatchadourian, Mehdi Bagherzadeh, Rhia Singh, Ajani Stewart, and Anita Raja (2021). “An Empirical Study of Refactorings and Technical Debt in Machine Learning Systems”. In: 2021 IEEE/ACM 43rd ICSE.

[4] Tom, Edith, Aybüke Aurum, and Richard Vidgen (2013). “An Exploration of Technical Debt”. In: Journal of Systems and Software.