2 citations · 4 across the 8 of their papers we have counts for
8 papers
Toward Individual Fairness Without Centralized Data: Selective Counterfactual Consistency for Vertical Federated Learning
Dawood Wasif, Chandan K. Reddy, Terrence J. Moore +1
When algorithmic decisions depend on data distributed across institutions, how can we ensure that an individual's outcome does not change arbitrarily based on a protected attribute…
Risk-Aware Human-in-the-Loop Framework with Adaptive Intrusion Response for Autonomous Vehicles
Dawood Wasif, Terrence J. Moore, Seunghyun Yoon +4
Autonomous vehicles must remain safe and effective when encountering rare long-tailed scenarios or cyber-physical intrusions during driving. We present RAIL, a risk-aware human-in-…
DASH: Deception-Augmented Shared Mental Model for a Human-Machine Teaming System
Zelin Wan, Han Jun Yoon, Nithin Alluru +6
We present DASH (Deception-Augmented Shared mental model for Human-machine teaming), a novel framework that enhances mission resilience by embedding proactive deception into Shared…
BioimageAIpub: a toolbox for AI-ready bioimaging data publishing
Stefan Dvoretskii, Anwai Archit, Constantin Pape +2
Modern bioimage analysis approaches are data hungry, making it necessary for researchers to scavenge data beyond those collected within their (bio)imaging facilities. In addition t…
DriveMind: A Dual Visual Language Model-based Reinforcement Learning Framework for Autonomous Driving
Dawood Wasif, Terrence J. Moore, Chandan K. Reddy +5
End-to-end autonomous driving systems map sensor data directly to control commands, but remain opaque, lack interpretability, and offer no formal safety guarantees. While recent vi…
Advancing Human-Machine Teaming: Concepts, Challenges, and Applications
Dian Chen, Han Jun Yoon, Zelin Wan +9
Human-Machine Teaming (HMT) is revolutionizing collaboration across domains such as defense, healthcare, and autonomous systems by integrating AI-driven decision-making, trust cali…
RESFL: An Uncertainty-Aware Framework for Responsible Federated Learning by Balancing Privacy, Fairness and Utility
Dawood Wasif, Terrence J. Moore, Jin-Hee Cho
Federated Learning (FL) has gained prominence in machine learning applications across critical domains by enabling collaborative model training without centralized data aggregation…
Empirical Analysis of Privacy-Fairness-Accuracy Trade-offs in Federated Learning: A Step Towards Responsible AI
Dawood Wasif, Dian Chen, Sindhuja Madabushi +3
Federated Learning (FL) enables collaborative model training while preserving data privacy; however, balancing privacy preservation (PP) and fairness poses significant challenges.…