Xilu Wang

Dr. Xilu Wang is a Surrey Future Fellow at the University of Surrey, affiliated with the School of Computer Science and Electronic Engineering. They are part of the Nature Inspired Computing and Engineering Research Group and the Surrey Institute for People-Centred Artificial Intelligence (PAI). Their research focuses on data-driven optimization, trustworthy AI, and machine learning, with specialties in multi-objective optimization, Bayesian optimization, evolutionary machine learning, and federated optimization. Dr. Wang holds a PhD and can be contacted via email.

Research interests include developing algorithms for fair, privacy-preserving machine learning and optimization. Key topics span neural architecture search, federated optimization, and transfer learning. Dr. Wang has supervised multiple postgraduate students, including Abel Alexander (University of Surrey), Adrian Kruse (Bielefeld University), and Scheu Louis Lamar (Bielefeld University), focusing on Bayesian optimization and federated learning applications.

Publications emphasize large-scale optimization, federated systems, and secure evolutionary algorithms. Notable works include surrogate-assisted optimization methods and privacy-preserving frameworks. Their contributions bridge theoretical advancements with practical applications in autonomous driving, SAR imagery, and resource-constrained computing.

Dr. Wang’s work has been featured in IEEE Transactions on Evolutionary Computation and other top-tier journals. They actively explore interdisciplinary collaborations, particularly in trustworthy AI and federated learning security. Current projects aim to enhance algorithmic fairness, scalability, and real-world deployment in complex systems.