Artificial Intelligence Privacy and Optimization Lab

Directed by Dr. Xinyue Zhang, our lab explores the intersection of artificial intelligence, privacy preservation, secure communications and quantum computing. We focus on federated learning, deep learning model compression, ensemble learning, explainable AI, and large language models (LLMs) to build trustworthy and efficient algorithms for tomorrow's intelligent systems.

Join Us

Artificial Intelligence

We investigate communication‑efficient federated learning, privacy preservation in machine learning, AI in medical applications and large language models.

Privacy

Our work extends to secure wireless communications, cyber‑physical systems and data‑driven optimizations, ensuring that sensitive information remains protected.

Optimization

We push the boundaries of quantum machine learning and explore privacy‑aware optimisation techniques for emerging computing platforms.

News

New Student Joining: We are thrilled to announce that M. Mushfique Hossain Shafin will join AIPO Lab as a Ph.D. student starting Fall 2025.
Paper Accepted: Our paper "RAG Pipeline for Domain Specific Applications: A Case Study in Disseminating Dementia Care Practices" by Cummings, Aaron; Zhang, Xinyue; Olaniran, Mercy; Akintomide, Modupe has been accepted to the ACM/IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE '25).
Paper Accepted: Our paper "A Machine Learning Approach for Emergency Detection in Medical Scenarios Using Large Language Models" has been accepted to the International Symposium on Intelligent Computing and Networking (ISICN 2025).
Paper Accepted: Our paper "FedKDShap: Enhancing Federated Learning via Shapley Values Driven Knowledge Distillation for Non‑IID Data" has been accepted to the WWW 2025 Companion Proceedings (pages 1744–1751). DOI: 10.1145/3701716.3717645.
NSF CRII Award: Dr. Xinyue Zhang received the prestigious NSF CRII Award (2024) for "Federated Learning with Enhanced Efficiency and Privacy Preservation over Heterogeneous Devices" (Award Abstract #2348417).
NSF Collaborative Research Award: Dr. Xinyue Zhang received an NSF award for "Collaborative Research: CISE MSI: RDP: CNS: Towards the Resilient NextG Network Design for Federated Learning over Mobile Devices" (2025-2026).

Prospective Students

AIPO Lab is always looking for motivated Ph.D. students who are passionate about advancing the state of the art in secure and trustworthy artificial intelligence. If you are interested in joining our team, please visit our prospective students page for more information on how to apply.

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