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Song Han
PRECISE's Safe Autonomy Seminar: MCUNet: Tiny Deep Learning on IoT Devices
November 17, 2022

Machine learning on tiny IoT devices based on microcontroller units (MCU) is appealing but challenging: the memory of microcontrollers is 2-3 orders of magnitude smaller even than mobile phones. I’ll present the MCUNet project, a framework that jointly designs the efficient neural architecture (TinyNAS) and the lightweight inference engine (TinyEngine), enabling ImageNet-scale inference on microcontrollers. Beyond inference, our…

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Max Z. Li
PRECISE's Safe Autonomy Seminar: Routing with Privacy for drone package delivery systems
October 21, 2022

Uncrewed aerial vehicles (UAVs), or drones, are increasingly being used to deliver goods from vendors to customers. To safely conduct these operations at scale, drones are required to broadcast position information as codified in remote identification (remote ID) regulations. However, location broadcast of package delivery drones introduces a privacy risk for customers using these delivery services: Third-party observers may leverage broadcast drone trajectories to link customers with their purchases, potentially resulting in a wide range of privacy risks. 

We propose a…

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Osbert Bastani
PRECISE's Safe Autonomy Seminar: Towards Verifiable Machine Learning
October 18, 2022

Machine learning models are increasingly being incorporated into real-world systems, targeting domains such as robotics, healthcare, and software systems. A key challenge is ensuring that such systems are trustworthy. I will describe two strategies for verifying correctness properties for such systems. The first strategy leverages ideas from statistical verification to provide provable correctness guarantees. In particular, we show how to quantify the uncertainty of any given model in a way that satisfies PAC correctness guarantees, and then leverage these uncertainties to ensure safety.…

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Yasser Shoukry
PRECISE's Safe Autonomy Seminar: Provably-Correct Neurosymbolic Controllers for Autonomous Cyber-Physical Systems
October 11, 2022

While conventional reinforcement learning focuses on designing agents that can perform one task, meta-learning aims, instead, to solve the problem of designing agents that can generalize to different tasks (e.g., environments, obstacles, and goals) that were not considered during the design or the training of these agents. In this spirit, we consider the problem of training a provably safe Neural Network (NN) controller for uncertain nonlinear dynamical systems that can generalize to new tasks that were not present in the training data while preserving strong safety and correctness…

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Xiali (Sharon) Hei
PRECISE Seminar: Investigate and Mitigate the Attacks Caused by Out-of-Band Signals
September 30, 2022

Sensing and actuation systems are entrusted with increasing intelligence to perceive the environment and react to it. Their reliability often relies on the trustworthiness of sensors. As process automation and robotics keep evolving, sensing methods such as pressure/temperature/motion sensing are extensively used in conventional systems and rapidly emerging applications. This talk aims to investigate the threats incurred by the out-of-band signals and discuss the low-cost defense methods against physical injection attacks on sensors. Dr. Hei will present the results from…