
Developed a built-in cooccurrenceMatrix function for GloVe embeddings within the apache/systemds repository, focusing on efficient natural language processing workflows. The implementation handled end-to-end co-occurrence matrix computation, integrating text cleaning, tokenization, and window-based weighting directly into the function. Leveraging DML and Java, the work included matrix encoding and a dedicated unit test to ensure correctness and stability of the computation path. This addition expanded SystemDS’s NLP capabilities, supporting streamlined GloVe embedding generation and potentially improving matrix operation performance. The approach demonstrated a strong grasp of data processing, machine learning, and software engineering principles in a production codebase.
May 2025: Delivered a built-in cooccurrenceMatrix function for GloVe in the apache/systemds repository, enabling efficient generation of GloVe co-occurrence matrices with integrated NLP preprocessing (text cleaning, tokenization) and window-based weighting, plus matrix encoding and a validation test.
May 2025: Delivered a built-in cooccurrenceMatrix function for GloVe in the apache/systemds repository, enabling efficient generation of GloVe co-occurrence matrices with integrated NLP preprocessing (text cleaning, tokenization) and window-based weighting, plus matrix encoding and a validation test.

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