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I am a PhD Student in the Faculty of Computer Science at Dalhousie University. I am currently being advised by Prof. Masud Rahman, and Prof. Foutse Khomh. My primary research interests are at the intersection of Software Engineering and Deep Learning, with a focus on secure, reliable, and explainable deep learning systems. My current research focuses on understanding the behaviour of bugs in deep learning systems and trying to improve the reproducibility of deep learning bugs.

My research interests focus on developing effective techniques to debug data bugs throughout the entire lifecycle in deep learning systems used for software engineering tasks. Specifically, I am interested in investigating bugs from errors in collecting, labelling, and preprocessing code, text, metric, and graph-based training data. My goals are to leverage existing software engineering research, large language models, and explainable AI (XAI) techniques to explain and accurately localize data defects, develop techniques to reproduce bugs in sandbox environments, and propose methods to repair data through model-data interactions. Overall, my research aims to enhance the reliability, robustness, and trustworthiness of safety-critical deep learning systems by addressing the key challenges in effectively debugging data bugs end-to-end.

Mehil Shah's Projects

bug_reproducibility_dl_bugs icon bug_reproducibility_dl_bugs

This repository serves as the replication package for the work "Towards Enhancing the Reproducibility of Deep Learning Bugs: An Empirical Study".

dlrepro icon dlrepro

Replication Package for the tool "dlRepro", which was built as a proof-of-concept tool in the paper "Towards Enhancing the Reproducibility of Deep Learning Bugs: An Empirical Study".

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