2 citations · 4 across the 9 of their papers we have counts for
9 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-…
MURIM: Multidimensional Reputation-based Incentive Mechanism for Federated Learning
Sindhuja Madabushi, Dawood Wasif, Jin-Hee Cho
Federated Learning (FL) has emerged as a leading privacy-preserving machine learning paradigm, enabling participants to share model updates instead of raw data. However, FL continu…
Multi-Agent Code-Orchestrated Generation for Reliable Infrastructure-as-Code
Rana Nameer Hussain Khan, Dawood Wasif, Jin-Hee Cho +1
The increasing complexity of cloud-native infrastructure has made Infrastructure-as-Code (IaC) essential for reproducible and scalable deployments. While large language models (LLM…
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…
OPUS-VFL: Incentivizing Optimal Privacy-Utility Tradeoffs in Vertical Federated Learning
Sindhuja Madabushi, Ahmad Faraz Khan, Haider Ali +1
Vertical Federated Learning (VFL) enables organizations with disjoint feature spaces but shared user bases to collaboratively train models without sharing raw data. However, existi…
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.…