ABSTRACT. Pushing autonomous systems to their operational limits reveals critical insights into the intersection of AI, real-time decision-making, and high-performance control. In this talk, I will discuss the design, architecture, and operational challenges behind Cavalier Autonomous Racing - world-record-holding autonomous Indy race car, which competes at the highest levels of AI-driven motorsport. We will explore the critical engineering decisions that enable autonomy at the edge of performance -balancing perception, planning, and control under extreme dynamic constraints. From high-fidelity simulation to real-world deployment, I will highlight the iterative co-design of software and hardware that allows an autonomous vehicle to operate at speeds exceeding 170 mph while maintaining safety and precision. The talk will also cover the unique challenges of system integration, from sensor fusion and real-time computing to vehicle dynamics and predictive control. Through this lens, autonomous racing offers insights that extend beyond the track, informing the design of next-generation AI-driven mobility solutions.

ABSTRACT. Safe planning and control of multi-robot performing complex tasks has been a challenging problem. Methods that offer guarantees on safety and mission satisfaction generally do not scale well. On the other hand, more computationally tractable approaches do not offer much in terms of safety guarantees. In this talk, I will present a family of robust and predictive motion planning and control methods that overcome these limitations for a wide variety of task objectives, represented using Signal Temporal Logic (STL). Starting from the given STL specification, we formulate a non-convex optimization problem, which can be efficiently solved to local optimality in both centralized and decentralized manners. We also formulate constraints which result in trajectories that can be tracked near perfectly by off-the-shelf lower level controllers. The performance and scalability of the methods will be demonstrated through multi-robot simulation studies and experiments on quadrotor aerial robots and non-holonomic ground robots. Finally, I will present ongoing work on extending these methods to systems with partially known dynamics.

ABSTRACT. In the IoT world, where data privacy and security are paramount, Federated Learning (FL) is a distributed solution for training Machine learning models in the IoT domain that ensures data privacy and security by processing the information locally without transferring sensitive sensor information. This study proposes a novel FL framework for IoT use cases that offers state-of-the-art security tools to solve security and privacy issues. The proposed framework consists of  Decentralized Attribute-Based Encryption (DABE) for decentralized authentication and data encryption, Homomorphic Encryption (HE) for safe computation with encrypted data, Secure Multi-Party Computation (SMPC) for privacy-aware collaborative computations, and blockchain for transparent communication, data integrity, and distributed ledger management. Data are encrypted locally on IoT devices with DABE, and initial models are trained on cloud servers using an immutable blockchain network, which supports peer-to-peer authentication. - The new model weights encrypted using HE are transferred to fog layers and aggregated using SMPC. FL server: The FL server cleans up the global model (difference privacy) to avoid data leakage and distributes it to IoT devices for deployment. It solves major problems in secure decentralized learning, privacy-preserving, efficient, and secure FL in IoT applications, and real-time analytics and security in dynamic IoT markets.

ABSTRACT. Accelerated by rapid advances in machine learning and AI, there has been tremendous success in the design of learning-enabled autonomous systems in areas such as autonomous driving, intelligent transportation, and robotics. However, these exciting developments are accompanied by new fundamental challenges that arise regarding the safety and reliability of these increasingly complex control systems in which sophisticated algorithms interact with unknown environments. In this talk, I will provide new insights and discuss exciting opportunities to address these challenges.

Imperfect learning algorithms, system unknowns, and uncertain environments require design techniques to rigorously account for uncertainties. I advocate for the use of conformal prediction (CP) — a statistical tool for uncertainty quantification — due to its simplicity, generality, and efficiency as opposed to existing optimization-based neural network verification techniques that are either conservative or not scalable, especially during runtime. I first provide an introduction to CP for the non-expert who is interested in applying CP to address real-world engineering problems. My goal is then to show how we can use CP to solve the problem of predicting failures of learning-enabled systems during their operation. Particularly, we leverage CP and design two predictive runtime verification algorithms (an accurate and an interpretable version) that compute the probability that a high-level system specifications is violated. Finally, we will discuss how we can use robust versions of CP to deal with distribution shifts that arise when the deployed learning-enabled system is different from the system during design time.