1 citations · 3 across the 18 of their papers we have counts for
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Large Language Models Lack Temporal Awareness of Medical Knowledge
Zihan Guan, Qiao Jin, Guangzhi Xiong +6
The existing methods for evaluating the medical knowledge of Large Language Models (LLMs) are largely based on atemporal examination-style benchmarks, while in reality, medical kno…
Agentic Framework for Epidemiological Modeling
Rituparna Datta, Zihan Guan, Baltazar Espinoza +5
Epidemic modeling is essential for public health planning, yet traditional approaches rely on fixed model classes that require manual redesign as pathogens, policies, and scenario…
Are LLMs Ready for Neural-integrated Mechanistic Modeling? A Benchmark and Agentic Framework
Zihan Guan, Rituparna Datta, Mengxuan Hu +5
Large language models (LLMs) have shown promise in constructing mechanistic models from data. However, existing evaluations largely focus on simplified settings and fail to capture…
Improving Hospital Risk Prediction with Knowledge-Augmented Multimodal EHR Modeling
Rituparna Datta, Jiaming Cui, Zihan Guan +5
Accurate prediction of clinical outcomes using Electronic Health Records (EHRs) is critical for early intervention, efficient resource allocation, and improved patient care. EHRs c…
Prediction of Hospital Associated Infections During Continuous Hospital Stays
Rituparna Datta, Methun Kamruzzaman, Eili Y. Klein +4
The US Centers for Disease Control and Prevention (CDC), in 2019, designated Methicillin-resistant Staphylococcus aureus (MRSA) as a serious antimicrobial resistance threat. The ri…
CALYPSO: Forecasting and Analyzing MRSA Infection Patterns with Community and Healthcare Transmission Dynamics
Rituparna Datta, Jiaming Cui, Gregory R. Madden +1
Methicillin-resistant Staphylococcus aureus (MRSA) is a critical public health threat within hospitals as well as long-term care facilities. Better understanding of MRSA risks, eva…
Improving Epidemic Analyses with Privacy-Preserving Integration of Sensitive Data
Zihan Guan, Zhiyuan Zhao, Fengwei Tian +5
Epidemic analyses increasingly rely on heterogeneous datasets, many of which are sensitive and require strong privacy protection. Although differential privacy (DP) has become a st…
Benign Samples Matter! Fine-tuning On Outlier Benign Samples Severely Breaks Safety
Zihan Guan, Mengxuan Hu, Ronghang Zhu +2
Recent studies have uncovered a troubling vulnerability in the fine-tuning stage of large language models (LLMs): even fine-tuning on entirely benign datasets can lead to a signifi…
Contrastive Explainable Clustering with Differential Privacy
Dung Nguyen, Ariel Vetzler, Sarit Kraus +1
This paper presents a novel approach to Explainable AI (XAI) that combines contrastive explanations with differential privacy for clustering algorithms. Focusing on k-median and k-…
Efficient PAC Learnability of Dynamical Systems Over Multilayer Networks
Zirou Qiu, Abhijin Adiga, Madhav V. Marathe +4
Networked dynamical systems are widely used as formal models of real-world cascading phenomena, such as the spread of diseases and information. Prior research has addressed the pro…
Learning the Topology and Behavior of Discrete Dynamical Systems
Zirou Qiu, Abhijin Adiga, Madhav V. Marathe +4
Discrete dynamical systems are commonly used to model the spread of contagions on real-world networks. Under the PAC framework, existing research has studied the problem of learnin…