1 citations · 3 across the 12 of their papers we have counts for
13 papers
FairDiffuseVQVAE: Sampling-Time Fairness in Tabular Diffusion via Conditional Refinement of Vector-Quantized Latents
Nitish Nagesh, Mahdi Bagheri, Amir M. Rahmani
Synthetic tabular data is increasingly used in privacy-preserving data sharing, data augmentation, and to mitigate downstream classifier bias. State-of-the-art tabular diffusion mo…
PerCaM-Health: Personalized Dynamic Causal Graphs for Healthcare Reasoning
Elahe Khatibi, Ziyu Wang, Saba A. Farahani +4
Personalized healthcare decisions require reasoning about how physiological and behavioral variables influence an individual patient over time. Existing temporal causal discovery m…
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…
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…
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…
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…
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…
FairTabGen: High-Fidelity and Fair Synthetic Health Data Generation from Limited Samples
Nitish Nagesh, Salar Shakibhamedan, Mahdi Bagheri +4
Synthetic healthcare data generation offers a promising solution to research limitations in clinical settings caused by privacy and regulatory constraints. However, current synthet…
FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation
Nitish Nagesh, Ziyu Wang, Amir M. Rahmani
Synthetic data generation creates data based on real-world data using generative models. In health applications, generating high-quality data while maintaining fairness for sensiti…
CDF-RAG: Causal Dynamic Feedback for Adaptive Retrieval-Augmented Generation
Elahe Khatibi, Ziyu Wang, Amir M. Rahmani
Retrieval-Augmented Generation (RAG) has significantly enhanced large language models (LLMs) in knowledge-intensive tasks by incorporating external knowledge retrieval. However, ex…
TransECG: Leveraging Transformers for Explainable ECG Re-identification Risk Analysis
Ziyu Wang, Elahe Khatibi, Kianoosh Kazemi +4
Electrocardiogram (ECG) signals are widely shared across multiple clinical applications for diagnosis, health monitoring, and biometric authentication. While valuable for healthcar…
Skewed Memorization in Large Language Models: Quantification and Decomposition
Hao Li, Di Huang, Ziyu Wang +1
Memorization in Large Language Models (LLMs) poses privacy and security risks, as models may unintentionally reproduce sensitive or copyrighted data. Existing analyses focus on ave…
HealthQ: Unveiling Questioning Capabilities of LLM Chains in Healthcare Conversations
Ziyu Wang, Hao Li, Di Huang +3
Effective patient care in digital healthcare requires large language models (LLMs) that not only answer questions but also actively gather critical information through well-crafted…