Speaker 1: Professor Weisi Guo - Head of Human Machine Intelligence Group, Cranfield University (30 mins + Q&A)

Title of talk: Key Research Highlights from Trustworthy Autonomy to Democratising Autonomy in 6G

Abstract: 4+ years of EPSRC Trustworthy Autonomous Systems and now 6G means we have learnt a lot about what the main research problems are in autonomy. Here, Weisi will present the key research highlights in his research group on: (i) attacking and securing drone communication channels and guidance systems, (ii) defending against attacks using physics-informed encryption, (iii) advancing autonomy through action-robust reinforcement learning, and (iv) improving regulatory and enforcement trust through inverse learning. He will then present his group’s work in 6G ORAN: (a) how to use LLMs to auto-specify 6G services, (b) how to verify cascade effects of innovators in ORAN, and (c) how LLMs can transform the way we optimise autonomy.

Bio: Prof Weisi Guo obtained MEng, MA, and PhD degrees from the University of Cambridge. Weisi was a Turing Fellow at the Alan Turing Institute from 2017. He has published over 160 journal papers in the areas of networks, AI, and autonomy. His key papers include: a Nature, Nature Communications, Nature Machine Intelligence, and several cover issues in Royal Society, Nature Publishing Group and IEEE Transactions journals. Weisi has been PI on over £8.1m and an investigator on over £35m of research funding. His group has been recipient of 4 Marie-Curie and RAEng fellowships. He currently co-leads the EPSRC 6G Future Communications Hub on Distributed Computing, EPSRC Trustworthy Autonomous Systems Security Node, and was the coordinator for EU Data Aware Wireless Networks. He has been an IET Innovation Award winner (2015) and been a runner-up in the Bell Labs Prize three times.

 

Speaker 2: Dr Ivan Petrunin , Reader in Signal Processing for Autonomous Systems, Cranfield University (30mins + Q&A)

Title of talk: “Last Mile” time dissemination and synchronization solutions for autonomous systems

Abstract: Accurate and trusted time information is essential in many national infrastructure sectors, such as energy, communication, and transport and nowadays it is predominantly supplied via the Global Navigation Satellite Systems (GNSS), which is vulnerable to natural and human factors. Critical reliance on this technology is illustrated in the Government’s Blackett Review, which estimates the economic impact from a large-scale GNSS outages as £1bn a day. The Government’s PNT Office highlights the importance of the timing in their Ten Points Plan outlining the need for growth policy in this area: R&D programmes, standards, and testing. This presentation highlights key research developments in this area at Cranfield and discusses further needs in development and testing of future resilient and trusted timing solutions for mobile users and autonomous systems.

Bio: Dr Ivan Petrunin received the Ph.D. degree in Applied Signal Processing from Cranfield University, in 2012. His expertise covers areas of signal processing and artificial intelligence for sensors, perception, data and information fusion, and decision-making with application to surveillance and resilient positioning, navigation and timing (PNT) solutions for ground and aerial systems. Ivan's research in this area has a particular emphasis on the enhancement of performance and safety of operations aspects by developing and employing techniques based on Artificial Intelligence. He is coordinating research around several major facilities at Cranfield: Instrumented Test Track, Research and Innovation Timing Node, PNT Simulation Facility and Holographic Radar and leading 7 projects (6 funded via European Space Agency) in the area of PNT.

 

Professor Weisi Guo, Head of Human Machine Intelligence Group, Cranfield University

Dr Ivan Petrunin , Reader in Signal Processing for Autonomous Systems, Cranfield University