most citedCDF-RAG: Causal Dynamic Feedback for Adaptive Retrieval-Augmented Generation

1 citations · 1 across the 5 of their papers we have counts for

collaborators

8 papers

cs.LG2026

Membership Inference Attacks Expose Participation Privacy in ECG Foundation Encoders

Ziyu Wang, Elahe Khatibi, Ankita Sharma +4

Foundation-style ECG encoders pretrained with self-supervised learning are increasingly reused across tasks, institutions, and deployment contexts, often through model-as-a-service…

cs.LG2026

CARE-ECG: Causal Agent-based Reasoning for Explainable and Counterfactual ECG Interpretation

Elahe Khatibi, Ziyu Wang, Ankita Sharma +4

Large language models (LLMs) enable waveform-to-text ECG interpretation and interactive clinical questioning, yet most ECG-LLM systems still rely on weak signal-text alignment and…

cs.LG2026

Evaluating Causal Discovery Algorithms for Path-Specific Fairness and Utility in Healthcare

Nitish Nagesh, Elahe Khatibi, Thomas Hughes +3

Causal discovery in health data faces evaluation challenges when ground truth is unknown. We address this by collaborating with experts to construct proxy ground-truth graphs, esta…

cs.CL2026

MedSpeak: A Knowledge Graph-Aided ASR Error Correction Framework for Spoken Medical QA

Yutong Song, Shiva Shrestha, Chenhan Lyu +5

Spoken question-answering (SQA) systems relying on automatic speech recognition (ASR) often struggle with accurately recognizing medical terminology. To this end, we propose MedSpe…

cs.CR2025

Linkage Attacks Expose Identity Risks in Public ECG Data Sharing

Ziyu Wang, Elahe Khatibi, Farshad Firouzi +3

The increasing availability of publicly shared electrocardiogram (ECG) data raises critical privacy concerns, as its biometric properties make individuals vulnerable to linkage att…

cs.CL2025

MedCoT-RAG: Causal Chain-of-Thought RAG for Medical Question Answering

Ziyu Wang, Elahe Khatibi, Amir M. Rahmani

Large language models (LLMs) have shown promise in medical question answering but often struggle with hallucinations and shallow reasoning, particularly in tasks requiring nuanced…