Upcoming Seminars

The Next Talk is on Dec. 17 by

Peng Wu, Northeastern University

Zoom: https://cityu.zoom.us/meeting/register/KfTefDbQTNa9C9qDgNNpWw

If you have any difficulty registering, please contact taoli96@ieee.org.

Schedule At a Glance

Bayesian Data Fusion for Distributed Learning

Dec. 17, 2025, 2:00–3:00 pm UTC (6–7 am US West, 9–10 am US East, 2–3 pm UK, 3–4 pm CET, 7:30–8:30 pm India, 10–11 pm China)

Abstract: This presentation explores two critical pillars of Trustworthy AI—Mixed Reality (MR) Assurance and Federated Learning (FL)—united by a focus on probabilistic methods for safety and privacy. First, we address the safety challenges in mission-critical MR applications, introducing a probabilistic verification framework that leverages Bayesian Networks to model the causal links between system parameters and physiological responses. This approach enables formal safety guarantees and real-time mitigation of cybersickness, moving beyond traditional reactive measures. Second, we tackle privacy and heterogeneity in distributed systems through Bayesian Clustered and Personalized FL, demonstrating how sharing probabilistic posteriors rather than raw data enables effective collaboration in applications such as indoor localization, jammer detection, and multi-agent reinforcement learning. Together, these contributions illustrate a unified path toward AI systems that are rigorously verified for human safety and secure in their data utilization.

Peng Wu, Northeastern University

Bio: Peng Wu is a Postdoctoral Researcher in the Department of Electrical and Computer Engineering at Northeastern University under the advice of Professor Mahdi Imani. His research focuses on security, privacy, and trustworthiness in distributed intelligence, with applications to mixed-reality (cognitive) attacks, multi-agent and robotic collaboration, and privacy-preserving machine learning. He develops methods at the intersection of machine learning, reinforcement learning, and federated learning to enable robust and reliable intelligent systems. He received his Ph.D. in Electrical Engineering from Northeastern University under the supervision of Professor Pau Closas, where his doctoral work advanced Bayesian and deep learning approaches for federated learning, applied to indoor positioning, Global Navigation Satellite Systems (GNSS), and image processing. He also holds a Master’s degree in Electrical Engineering at Northeastern University. His work has led to publications in leading journals and conferences, including IEEE Transactions on Signal Processing, IEEE Transactions on Aerospace and Electronic Systems, and the IEEE/ION Position, Location and Navigation Symposium (PLANS), where he received a Best Paper Award in 2023. Beyond academia, Peng co-founded Cactivate, an AI-driven platform that optimizes and automates online advertising for agencies and e-commerce businesses, serving as its Chief Scientist.

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