Plamen Angelov

Organization: Lancaster Intelligent, Robotic and Autonomous systems (LIRA) Centre, Lancaster University
Homepage: https://www.lancaster.ac.uk/lira/people/plamen-angelov

Prof. Angelov (PhD 1993, DSc 2015) holds a Personal Chair in Intelligent Systems at Lancaster University and is a Fellow of the IEEE, IET, AAIA and of ELLIS. He is member-at-large of the Board of Governors (BoG) of the International Neural Networks Society (INNS) and of the Systems, Man and Cybernetics Society of the IEEE (SMC-S) as well as Program co-Director of the Human-Centered Machine Learning for ELLIS. He has 400 publications in leading journals, peer-reviewed conference proceedings, 3 granted patents, 3 research monographs (published by Springer (2002 and 2018) and Wiley, 2012) cited over 15000 times (h-index 63). Prof. Angelov has an active research portfolio in the area of explainable deep learning and its applications to autonomous driving, Earth Observation and pioneering results in online learning from streaming data and evolving systems. His research was recognised by multiple awards including 2020 Dennis Gabor award "for outstanding contributions to engineering applications of neural networks". He is the founding co-Editor-in-Chief of Springer’s journal on Evolving Systems and Associate Editor of other leading scientific journals, including IEEE Transactions (IEEE-T) on Cybernetics, IEEE-T on Fuzzy Systems, IEEE-T on AI. He gave over 30 keynote talks and co-organised and co-chaired over 30 IEEE conferences (including several IJCNN), workshops at NeurIPS, ICCV, PerCom and other leading conferences. Prof Angelov chaired the Standards Committee of the Computational Intelligent Society of the IEEE initiating the IEEE standard on explainable AI (XAI). More details can be found at https://www.lancaster.ac.uk/lira/people/plamen-angelov

KEYNOTE TALK: Bringing Deep Learning and Reasoning Closer

Zeng-Guang Hou

Organization: State Key Lab of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences
Homepage: http://www.cebsit.ac.cn/sourcedb_cebsit_cas/zw/rck/Members/202007/t20200722_5641901.html

Zeng-Guang Hou is a Professor and Deputy Director of the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences (CAS). He is a VP of Chinese Association of Automation (CAA), VP of the Asia Pacific Neural Network Society (APNNS). Dr. Hou is an IEEE Fellow. He also serves as an AE of IEEE Transactions on Cybernetics, and an Editorial Board Member of Neural Networks. Dr. Hou was a recipient of the Dennis Gabor Award of the International Neural Network Society (INNS) in 2023, the Outstanding Achievement Award of Asia Pacific Neural Network Society (APNNS) in 2017, and IEEE Transactions on Neural Networks Outstanding Paper Award in 2013, etc. His research interests include computational intelligence, robotics and intelligent systems.

KEYNOTE TALK: AI and BCI Based Interaction Control Methods for Robotics

Giacomo Indiveri

Organization: University of Zurich and ETH Zurich, Switzerland
Homepage: https://www.ini.uzh.ch/en/institute/people?uname=giacomo

Giacomo Indiveri is the director of the Institute of Neuroinformatics of the University of Zurich (UZH) and ETH Zurich and a dual Professor of Neuromorphic Cognitive Systems at UZH and ETH Zurich. He obtained an M.Sc. degree in electrical engineering in 1992 and a Ph.D. degree in computer science in 2004 from the University of Genoa, Italy. Prof. Indiveri has also expertise in neuroscience, computer science, and machine learning. He has been combining these disciplines by studying natural and artificial intelligence in neural processing systems and in neuromorphic cognitive agents. His latest research interests lie in the study of spike-based learning mechanisms and recurrent networks of biologically plausible neurons, and in their integration in real-time closed-loop sensory-motor systems designed using analog/digital circuits and emerging memory technologies. His group uses these neuromorphic circuits to validate brain inspired computational paradigms in real-world scenarios, and to develop a new generation of fault-tolerant event-based neuromorphic computing technologies. Indiveri is senior member of the IEEE society, and a recipient of the 2021 IEEE Biomedical Circuits and Systems Best Paper Award. He is also an ERC fellow, recipient of three European Research Council grants.

KEYNOTE TALK: Neuromorphic Intelligence: spiking neural network and on-line learning circuits for brain-inspired technologies.

For many practical tasks that involve real-time processing of sensory data and closed-loop interactions with the environment, conventional and artificial intelligence technologies cannot match the performance of biological ones. One of the reasons for this gap is that neural computation in biological systems is organized in a way that is very different from the way it is implemented in today's deep networks. In biological neural systems computation is tightly linked to the properties of their computational embodiment, to the physics of their computing elements and to their temporal dynamics. A promising approach that closely emulates principles of computation of animal brains is that of neuromorphic intelligence. In this talk I will show how this approach can provide useful tools for investigating computational models of neural processing while at the same time offering a technology that can complement standard AI approaches for low-power sensory processing at the edge.