Machine Learning Methods for Trustworthy Autonomous Systems

There is a fast development of different machine learning methods – for object classification, tracking, action recognition and other tasks with multiple types of data – from images and videos to data from wireless sensor networks. Autonomous image and video analytics faces a number of challenges due to the huge volumes of data that sensors provide, the changeable environmental conditions and other factors. However, it is important to know when the methods work well and when they are not reliable, e.g. how much could we trust the obtained results? How could we characterise trust is a related question. How could we quantify the impact of uncertainties on the developed solutions? This talk will discuss current trends in the area of machine learning and show results for image and video analytics for autonomous systems. This talk will present recent results on automated behaviour analysis for decision making. Recent results for automated video analytics will be presented with Gaussian process methods, deep learning and other methods. Their pros and cons will be discussed. Some of these results are part of Digital twins, recently developed new tools that incorporate machine learning and artificial intelligence methods. This talk will discuss the big potential of Digital Twins, the opportunities and challenges that they bring.

Biography

Lyudmila Mihaylova is Professor of Signal Processing and Control in the Department of Automatic Control and Systems Engineering at the University of Sheffield, Sheffield, United Kingdom. Her research interests are in the areas of trustworthy autonomous systems with applications to smart cities, sensor networks, digital health and others. She has expertise in the areas of machine learning, intelligent sensing and sensor data fusion. She won the Tammy Blair best award from the International Conference of Information Fusion 2017, best paper awards from the IEEE DESSERT’2019, 17th IEEE SPA’2013 Conference and IEEE Sensor Data Fusion Workshop, 2013. Prof. Mihaylova has published more than 200 scientific papers in peer reviewed international journals such as IEEE Transactions on Aerospace and Electronic Systems, IEEE Transactions on Signal Processing, Automatica, IEEE Transactions on Industrial informatics and in a number of conferences. She has more than 6200 citations on google scholar. She is Associate Editor-in-Chief for the IEEE Transactions on Aerospace and Electronic Systems, Senior Editor for the Target Tracking and Multi-sensor data Fusion area since 2021, and a Subject Area Editor for the Elsevier Signal Processing Journal since 2022. She is a guest Editor for a special issue for Frontiers of Robotics and AI (2022-2023). Prof. Mihaylova is on the Board of Directors of the International Society of Information Fusion (ISIF) and was the ISIF President in the period 2016–2018. She has given a number of talks and tutorials, including NATO SET- 262 AI 2018 (Hungary), Fusion 2017 (Xi’an, China), plenary talks for the IEEE Sensor Data Fusion 2015 (Germany), invited talks at IPAMI Traffic Workshop 2016 (USA) and others. She is a member of the organising committee of the International Conference of Information Fusion 2022, 2021, IEEE MFI’ 2021, UKCI’ 2021 and vice-chair of the UKCI 2022. She was the general vice-chair for the International Conference on Information Fusion 2018 (Cambridge, UK), of the IET Data Fusion & Target Tracking 2014 and 2012 Conferences, publications chair for ICASSP 2019 (Brighton, UK) and others.