27 citations · 47 across the 14 of their papers we have counts for
15 papers
PersonaDrive: Human-Style Retrieval-Augmented VLA Agents for Closed-Loop Driving Simulation
Mahmoud Srewa, Praneetsai Iddamsetty, Mohammad Abdullah Al Faruque +1
Closed-loop driving simulators typically populate their environments with non-ego traffic agents that behave largely the same way, produced either by rule-based traffic managers or…
APPA: Adaptive Preference Pluralistic Alignment for Fair Federated RLHF of LLMs
Mahmoud Srewa, Tianyu Zhao, Salma Elmalaki
Aligning large language models (LLMs) with diverse human preferences requires pluralistic alignment, where a single model must respect the values of multiple distinct groups simult…
PACIFIC: Can LLMs Discern the Psychometric Traits Influencing Your Preferences? Personality-Driven Preference Alignment in LLMs
Tianyu Zhao, Siqi Li, Yasser Shoukry +1
User preferences are increasingly used to personalize Large Language Model (LLM) responses, yet reliably leveraging preference signals remains under-explored. In practice, preferen…
A Systematic Evaluation of Preference Aggregation in Federated RLHF for Pluralistic Alignment of LLMs
Mahmoud Srewa, Tianyu Zhao, Salma Elmalaki
This paper addresses the challenge of aligning large language models (LLMs) with diverse human preferences within federated learning (FL) environments, where standard methods often…
PluralLLM: Pluralistic Alignment in LLMs via Federated Learning
Mahmoud Srewa, Tianyu Zhao, Salma Elmalaki
Ensuring Large Language Models (LLMs) align with diverse human preferences while preserving privacy and fairness remains a challenge. Existing methods, such as Reinforcement Learni…
MoCoMR: A Collaborative MR Simulator with Individual Behavior Modeling
Diana Romero, Fatima Anwar, Salma Elmalaki
Studying collaborative behavior in Mixed Reality (MR) often requires extensive, challenging data collection. This paper introduces MoCoMR, a novel simulator designed to address thi…
FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk
Tianyu Zhao, Mahmoud Srewa, Salma Elmalaki
Federated Learning (FL) inherently mitigates mass data centralization risks; however, its privacy protections are not equally distributed - leaving vulnerable individuals dispropor…
PEARL: Structural Privacy-Utility Control in Human-Centric CPS via Personalized Early-Exit Deep Reinforcement Learning
Mojtaba Taherisadr, Salma Elmalaki
In human-centric Cyber-Physical Systems (CPS), personalized Deep Reinforcement Learning (DRL) agents must share fine-grained control actions with cloud services, exposing sensitive…
FinA: Fairness of Adverse Effects in Decision-Making of Human-Cyber-Physical-System
Tianyu Zhao, Salma Elmalaki
Ensuring fairness in decision-making systems within Human-Cyber-Physical-Systems (HCPS) is a pressing concern, particularly when diverse individuals, each with varying behaviors an…
Towards Fairness-aware Crowd Management System and Surge Prevention in Smart Cities
Yixin Zhang, Tianyu Zhao, Salma Elmalaki
Instances of casualties resulting from large crowds persist, highlighting the existing limitations of current crowd management practices in Smart Cities. One notable drawback is th…
FAIRO: Fairness-aware Adaptation in Sequential-Decision Making for Human-in-the-Loop Systems
Tianyu Zhao, Mojtaba Taherisadr, Salma Elmalaki
Achieving fairness in sequential-decision making systems within Human-in-the-Loop (HITL) environments is a critical concern, especially when multiple humans with different behavior…
adaPARL: Adaptive Privacy-Aware Reinforcement Learning for Sequential-Decision Making Human-in-the-Loop Systems
Mojtaba Taherisadr, Stelios Andrew Stavroulakis, Salma Elmalaki
Reinforcement learning (RL) presents numerous benefits compared to rule-based approaches in various applications. Privacy concerns have grown with the widespread use of RL trained…
ERUDITE: Human-in-the-Loop IoT for an Adaptive Personalized Learning System
Mojtaba Taherisadr, Mohammad Abdullah Al Faruque, Salma Elmalaki
Thanks to the rapid growth in wearable technologies and recent advancement in machine learning and signal processing, monitoring complex human contexts becomes feasible, paving the…
Future of Smart Classroom in the Era of Wearable Neurotechnology
Mojtaba Taherisadr, Berken Utku Demirel, Mohammad Abdullah Al Faruque +1
Interdisciplinary research among engineering, computer science, and neuroscience to understand and utilize the human brain signals resulted in advances and widespread applicability…
FaiR-IoT: Fairness-aware Human-in-the-Loop Reinforcement Learning for Harnessing Human Variability in Personalized IoT
Salma Elmalaki
Thanks to the rapid growth in wearable technologies, monitoring complex human context becomes feasible, paving the way to develop human-in-the-loop IoT systems that naturally evolv…