Upcoming Seminars

Abstract: Autonomous UAVs increasingly operate in environments where sensing is imperfect, communication is intermittent, and uncertainty is unavoidable. In this talk, I will present a control–learning co-design framework that addresses these challenges by explicitly accounting for perception limitations in the control loop, rather than treating them as external disturbances.

The talk is structured around my dissertation thrusts: Learn to Track, Learn to Localize, and Learn to Evade. I will describe how adaptive neural perception can be coupled with UAV motion to reduce inference latency while maintaining reliable tracking, how cooperative localization can be sustained under GPS degradation through learning-based failure recovery, and how probabilistic sensor modeling can be integrated into obstacle-aware MPC for safe navigation around static and moving obstacles, including other UAVs. Through examples from real-world experiments and simulations, I will show how combining model-based control with learning-based perception improves robustness without sacrificing interpretability or real-time guarantees. The talk will conclude with open problems and design principles for building resilient and trustworthy cyber-physical systems that operate under persistent uncertainty.

Abstract: Positioning and tracking are key enablers of a wide range of applications that require reliable and accurate location information. However, these technologies are vulnerable to both intentional attacks and unintentional interference, motivating the need for resilient solutions. This talk presents probabilistic, uncertainty-aware approaches to two fundamental challenges that threaten situational awareness in today’s hostile environments. First, we study resilient satellite-based navigation under infrastructure outages. We propose cooperative positioning strategies in large-scale real-time kinematic networks that achieve centimeter-level accuracy despite missing or mixed-quality reference data, and analyze performance as a function of network size, geometry, and robustness to outliers from jamming attacks and multipath. Second, we counter deception jamming in radar-based localization using multi-target tracking (MTT) frameworks based on random finite set theory. In particular, we focus on range gate pull-off attacks, which generate adversarial radar returns intended to deceive the tracker into following false targets. By exploiting attack characteristics within the MTT algorithm, we significantly reduce spoofed track persistence. Together, these contributions provide a path toward resilient navigation and sensing systems through probabilistic and Bayesian methods.