Philippe Durand

CNAM- Paris

Durand is Senior Lecturer in the Mathematics and Statistics Department of the National Conservatory of Arts and Crafts in the Mathematical and Numerical Modeling Department (M2N), and he works on the interaction between mathematical engineering and theoretical tools of mathematics like algebraic topology which usage has been increasing since the introduction of modern mathematics in the early sixties. He is interested in the mathematization of gauge theories in physics and string theory, and the application of topological and statistical methods to image processing. In the past, he invested in different methods of pattern recognition, and in particular the tools of mathematical morphology for the extraction of texture information. His recent works currently concern the use of topological data analysis and different approaches to applying classical or quantum neural networks to image processing. He published his results in various journals of applied mathematics, mathematic engineering, computer science and various proceedings of image processing conferences.

Invited Speech Title: Prediction of meteoric phenomena by a Quantum Neural and TDA approach

In this presentation, we propose original methods to predict the evolution of an exceptional storm disturbance, evolving in the form of Arcus between 6 a.m. and 8 a.m. and having caused a lot of damage on the west coast of Corsica on August 18, 2022. We compare the results combining a hybrid architecture consisting of a LSTM neural network and a classical CNN and a quantum neural network (QNN) which exploits quantum circuits on a series of Eumetstat satellite data from METEO France and C.A.P.E. data. The prediction in the case of the hybrid quantum neural network is of better quality than those given by the classical approach and above all faster. This approach is intended to be crossed by previous works from the topological data analysis (TDA) and persistence diagrams.

Shuaifeng Zhi

National University of Defense Technology (NUDT), China

Homepage: https://shuaifengzhi.

Shuaifeng Zhi currently a Lecturer (Assistant Professor) at the Department of Electronic Science and Technology, National University of Defense Technology (NUDT), China. He is selected for the 10th Youth Talent Support Program of China Association for Science and Technology (CAST) and the grantee of Hunan Provincial Natural Science Foundation for the Excellent Young Scientists Fund. He received the PhD degree in computing research from the Dyson Robotics Laboratory, Imperial College London in 2021, supervised by Prof. Andrew J. Davison and Prof. Stefan Leutenegger. He obtained the MSc.Eng and B.Eng from NUDT in 2017 and 2015, respectively. He was also a CSC-funded 6-month visiting student in 5GIC, University of Surrey in 2015. His current research interests focus on robot vision, particularly on scene understanding, neural scene representation, and semantic SLAM. He has published more than 10 papers from ICCV (Oral), CVPR, ICLR, IEEE T-PAMI, IEEE RA-L, etc.