Upcoming Seminars

Abstract: Modern societies increasingly rely on AI-driven cyber-physical-human systems (CPHSs), such as intelligent transportation, industrial automation, and other critical infrastructure. While these systems promise efficiency and intelligence, they also introduce new vulnerabilities where security, privacy, and resilience are tightly coupled with human trust. A central question arises: how can we design socio-technical systems that remain trustworthy and resilient even in the presence of adversarial manipulation and the cognitive biases of human decision-makers? In this talk, we will present a research agenda that develops principled and computationally tractable frameworks for understanding trust in CPHSs. We will walk through four key perspectives: assessing trust via meta-game analysis of human–CPS interactions, building trust in AI through crowd auditing and accountability mechanisms, exploiting trust in adversaries through defensive deception, and maintaining user trust under misinformation with information design strategies. These frameworks will be illustrated through case studies in critical CPHS domains. We will conclude by outlining future directions toward resilient, cognitive-aware CPHSs.

Abstract: Bayesian data fusion offers a principled route to distributed learning under privacy and uncertainty. This talk develops a unifying framework that clarifies how local beliefs should be combined when priors are shared. We analyze the Conditionally Independent Likelihood (CIL) and Conditionally Independent Posterior (CIP) rules, identify the prior double-counting pitfall in naïve posterior multiplication, and derive corrections that preserve coherence while characterizing accuracy as a function of client count and prior informativeness, beyond Gaussian models. Building on this foundation, we introduce federated posterior sharing for multi-agent systems, in which agents exchange posteriors rather than data to construct a global belief and act. The method supports single-shot or periodic synchronization, avoids prior reuse, and improves reward and sample efficiency under uncertainty and heterogeneity. Finally, we present a Bayesian formulation of clustered federated learning that treats client–cluster assignment as latent data association, yielding practical approximations that handle non-IID feature and label skew and outperform standard clustered FL. Together, these results provide a coherent recipe—fuse beliefs, correct for shared priors, and quantify uncertainty—for privacy-preserving learning and decision making at scale.