13 citations · 17 across the 11 of their papers we have counts for
9 papers · 1 filter
A Unified Evaluation Framework for Trustworthy Large Language Models, Agentic AI, and Multimodal Systems
Shaina Raza, Ahmed Y. Radwan, Imran Liaquat +1
Benchmark scores alone provide an incomplete basis for assessing the trustworthiness of modern artificial intelligence systems. Large language models (LLMs), agentic systems, and m…
FairLens: Benchmarking Fairness in Vision-Language Models for High-Stakes Decision-Making
Vahid Reza Khazaie, Ahmed Y. Radwan, Shaina Raza
Vision-language models (VLMs) are increasingly used to make decisions from visual inputs. We introduce FAIRLENS, a benchmark and evaluation framework for measuring both the fairnes…
Contrastive Predictive Coding with Compression for Enhanced Channel State Feedback in Wireless Networks
Ahmed Y. Radwan, Fahad Syed Muhammad, Matthew Baker +1
Accurate and timely channel state information (CSI) is essential for next-generation wireless systems, yet existing works treat CSI compression and CSI prediction as separate probl…
UnBias-Plus: Detect, Explain, and Rewrite Bias
Ahmed Y. Radwan, Ahmed ElKady, Sindhuja Chaduvula +3
Bias in natural language remains a persistent challenge in both human-written and AI-generated content, affecting domains such as journalism, education, and AI research. Most exist…
MU-SHOT-Fi: Self-Supervised Multi-User Wi-Fi Sensing with Source-free Unsupervised Domain Adaptation
Ahmed Y. Radwan, Hina Tabassum
Deep learning has been widely adopted for WiFi CSI-based human activity recognition (HAR) due to its ability to learn spatio-temporal features in a privacy-preserving and cost-effe…
From Features to Actions: Explainability in Traditional and Agentic AI Systems
Sindhuja Chaduvula, Jessee Ho, Kina Kim +6
Over the last decade, Explainable AI has primarily focused on interpreting individual model predictions, producing post-hoc explanations that relate inputs to outputs under a fixed…
SONIC-O1: A Real-World Benchmark for Evaluating Multimodal Large Language Models on Audio-Video Understanding
Ahmed Y. Radwan, Christos Emmanouilidis, Hina Tabassum +2
Multimodal Large Language Models (MLLMs) are a major focus of recent AI research. However, most prior work focuses on static image understanding, while their ability to process seq…
Position: Sustainable Open-Source AI Requires Tracking the Cumulative Footprint of Derivatives
Shaina Raza, Iuliia Zarubiieva, Ahmed Y. Radwan +4
Open-source AI is scaling rapidly, and model hubs now host millions of artifacts. Each foundation model can spawn large numbers of fine-tunes, adapters, quantizations, merges, and…
Reducing Hallucinations in LLMs via Factuality-Aware Preference Learning
Sindhuja Chaduvula, Ahmed Y. Radwan, Azib Farooq +2
Preference alignment methods such as RLHF and Direct Preference Optimization (DPO) improve instruction following, but they can also reinforce hallucinations when preference judgmen…