Research

Towards Fair and Efficient De-identification: Quantifying the Efficiency and Generalizability of De-identification Approaches

Accepted to the Findings of The 19th Conference of the European Chapter of the Association for Computational Linguistics 2026 2026

This work shows LLM-based de-identification often modifies clinical details. Current metrics to capture such changes are not expert-validated. We propose an expert-validated metric to test over-redaction.

Not What the Doctor Ordered: Surveying LLM-based De-identification and Quantifying Clinical Information Loss

The 2025 Conference on Empirical Methods in Natural Language Processing 2025

This work shows LLM-based de-identification often modifies clinical details. Current metrics to capture such changes are not expert-validated. We propose an expert-validated metric to test over-redaction.

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AROhI: an Interactive Toolkit for Estimating ROI of Data Analytics

IEEE 15th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, USA 2025

This work details a comprehensive tool that provides conventional and advanced ML approaches for demonstration using requirements dependency extraction and their ROI analysis as use case.

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