6 citations · 9 across the 11 of their papers we have counts for
13 papers
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…
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…
ECG Unveiled: Analysis of Client Re-identification Risks in Real-World ECG Datasets
Ziyu Wang, Anil Kanduri, Seyed Amir Hossein Aqajari +5
While ECG data is crucial for diagnosing and monitoring heart conditions, it also contains unique biometric information that poses significant privacy risks. Existing ECG re-identi…
Enhancing Performance and User Engagement in Everyday Stress Monitoring: A Context-Aware Active Reinforcement Learning Approach
Seyed Amir Hossein Aqajari, Ziyu Wang, Ali Tazarv +6
In today's fast-paced world, accurately monitoring stress levels is crucial. Sensor-based stress monitoring systems often need large datasets for training effective models. However…
Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection
Ziyu Wang, Zhongqi Yang, Iman Azimi +1
Mental health conditions, prevalent across various demographics, necessitate efficient monitoring to mitigate their adverse impacts on life quality. The surge in data-driven method…