papers

Publications (27)

cs.CL2026

PLACID: Privacy-preserving Large language models for Acronym Clinical Inference and Disambiguation

Manjushree B. Aithal, Ph. D., Alexander Kotz +2

Large Language Models (LLMs) offer transformative solutions across many domains, but healthcare integration is hindered by strict data privacy constraints. Clinical narratives are…

stat.AP2020

A Robust Statistical method to Estimate the Intervention Effect with Longitudinal Data

Mohammad M. Islam, Ph. D., Erik L. Heiny +1

Segmented regression is a standard statistical procedure used to estimate the effect of a policy intervention on time series outcomes. This statistical method assumes the normality…

cs.NI2023

Harnessing Digital Twin Technology for Adaptive Traffic Signal Control: Improving Signalized Intersection Performance and User Satisfaction

Sagar Dasgupta, Mizanur Rahman, Ph. D. +2

In this study, a digital twin (DT) technology based Adaptive Traffic Signal Control (ATSC) framework is presented for improving signalized intersection performance and user satisfa…

cs.CV2018

Wearable-based Mediation State Detection in Individuals with Parkinson's Disease

Murtadha D. Hssayeni, Michelle A. Burack, M. D. +3

One of the most prevalent complaints of individuals with mid-stage and advanced Parkinson's disease (PD) is the fluctuating response to their medication (i.e., ON state with maximu…

cs.CR2025

X-Guard: Multilingual Guard Agent for Content Moderation

Bibek Upadhayay, Vahid Behzadan, Ph. D

Large Language Models (LLMs) have rapidly become integral to numerous applications in critical domains where reliability is paramount. Despite significant advances in safety framew…

cs.CV2025

Read the Room: Inferring Social Context Through Dyadic Interaction Recognition in Cyber-physical-social Infrastructure Systems

Cheyu Lin, John Martins, Katherine A. Flanigan +1

Cyber-physical systems (CPS) integrate sensing, computing, and control to improve infrastructure performance, focusing on economic goals like performance and safety. However, they…