Publication: Developing an Equitable Machine Learning-Based Music Intervention for Older Adults At Risk for Alzheimer Disease: Protocol for Algorithm Development and Validation

Authors: Chelsea S Brown, Luna Dziewietin, Virginia Partridge, Jennifer Rae Myers Abstract Background: Given the high prevalence and cost of Alzheimer disease (AD), it is crucial to develop equitable interventions to address lifestyle factors associated with AD incidence (eg, depression). While lifestyle interventions show promise for reducing cognitive decline, culturally sensitive interventions are needed to ensure acceptability and engagement. Given the increased risk for AD and health care barriers among rural-residing older adults, tailoring interventions to align with rural culture and distinct needs is important to improve accessibility and adherence. Objective: This protocol aims to develop an intelligent recommendation system capable of identifying the optimal therapeutic music components to elicit engagement and resonate with diverse rural-residing older adults at risk for AD. Aim 1 is to develop culturally inclusive user personas for rural-residing older adults to understand…

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Oral Presentation: Alzheimer’s Association International Conference 2025 – Technology And Dementia Preconference

Jennifer Flexman presented "Improving Access to Dementia Care though AI-Powered Cognitive Rehabilitation Therapy" at the Technology And Dementia Preconference during Session 5: Data Blitz.

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Patent Awarded: AI powered mobility assessment system (No. US 12,343,138 B2)

Abstract: To assess the mobility of a user, a mobility assessment system obtains a video of a user having a plurality of video frames from a camera. The mobility assessment system generates a three-dimensional (3D) skeleton model of the user based on the plurality of video frames, and determines a range of motion of the user based on a change in position of the 3D skeleton model over the plurality of video frames. Then the mobility assessment system provides an indication of the range of motion of the user for display. Also, the mobility assessment system delivers tailored exercises and suggestions to enhance user mobility and reduce the risk of falls. Source: US-12343138-B2

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Publication: Reinforcement Learning on Dyads to Enhance Medication Adherence

Authors: Ziping Xu, Hinal Jajal, Sung Won Choi, Inbal Nahum-Shani, Guy Shani, Alexandra M. Psihogios, Pei-Yao Hung, Susan A. Murphy Abstract Medication adherence is critical for the recovery of adolescents and young adults (AYAs) who have undergone hematopoietic cell transplantation. However, maintaining adherence is challenging for AYAs after hospital discharge, who experience both individual (e.g. physical and emotional symptoms) and interpersonal barriers (e.g., relational difficulties with their care partner, who is often involved in medication management). To optimize the effectiveness of a three-component digital intervention targeting both members of the dyad as well as their relationship, we propose a novel Multi-Agent Reinforcement Learning (MARL) approach to personalize the delivery of interventions. By incorporating the domain knowledge, the MARL framework, where each agent is responsible for the delivery of one intervention component, allows for faster learning…

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Publication: Proceedings from SOUPS ’25 – Can You Walk Me Through It? Explainable SMS Phishing Detection using LLM-based Agents

Authors: Yizhu Wang, Haoyu Zhai, Chenkai Wang, Qingying Hao, Nick A Cohen, Roopa Foulger, Jonathan A Handler, and Gang Wang. Abstract SMS phishing poses a significant threat to users, especially older adults. Existing defenses mainly focus on phishing detection, but often cannot explain why the SMS is malicious to lay users. In this paper, we use large language models (LLMs) to detect SMS phishing while generating evidence-based explanations. The key challenge is that SMS is short, lacking the necessary context for security reasoning. We develop a prototype called SmishX which gathers external contexts (e.g., domain and brand information, URL redirection, and web screenshots) to augment the chain-of-thought (CoT) reasoning of LLMs. Then, the reasoning process is converted into a short explanation message to help users with their decision-making. Evaluation using real-world SMS datasets shows SmishX can achieve…

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Publication: Causal Directed Acyclic Graph-informed Reward Design

Authors: Luton Zou, Ziping Xu, Daiqi Gao, Susan Murphy Abstract It is well known that in reinforcement learning (RL) different reward functions may lead to the same optimal policy, while some reward functions can be substantially easier to learn. In this paper, we propose a framework for reward design by constructing surrogate rewards with mediators informed by causal directed acyclic graphs (DAGs), which are often available in real-world applications through domain knowledge. We show that under the surrogacy assumption, the proposed reward is unbiased and has lower variance than the primary reward. Specifically, we use an online reward design agent that adaptively learns the target surrogate reward in an unknown environment. Feeding the surrogate rewards to standard online learning oracles, we show that the regret bound can be improved. Our framework provides a theoretical improvement…

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