Prof. Jerry Chun-Wei Lin, Western Norway University of Applied Sciences, Norway

Jerry Chun-Wei Lin (Senior Member, IEEE) received the Ph.D. degree from the Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan.,He is currently a Full Professor with the Department of Computer Science, Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences, Bergen, Norway. He is also the Project Leader of SPMF, an open-source data mining library, which is a toolkit offering multiple types of data mining algorithms. He has published more than 500 research papers in refereed journals and international conferences. His research interests include data mining, soft computing, artificial intelligence, social computing, multimedia and image processing, and privacy-preserving and security technologies.,Prof. Lin also serves as the Editor-in-Chief for the International Journal of Data Science and Pattern Recognition and an Associate Editor for several top-tier journals, including IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics, and IEEE Transactions on Dependable and Secure Computing. He is the Fellow of IET and an ACM Distinguished Scientist.

Prof. Jianhua Zhang, Oslo Metropolitan University, Norway

Jianhua Zhang is has been Professor at Department of Computer Science, Oslo Metropolitan University, Norway, since 2018. From 2007-2017 he was Professor with School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China. From 2017 to 2018 he was Scientific Director at Vekia (a machine learning driven IT company), Lille, France.
Dr Zhang received his PhD in electrical engineering and computer science from Ruhr University Bochum, Germany, in 2005 and did postdoctoral research at Intelligent Systems Research Lab, University of Sheffield, UK, from 2005 to 2006. He was a Guest Scientist at TU Dresden, Germany, from 2002 to 2003 and Visiting Professor at TU Berlin, Germany between 2008 and 2015 and the University of Catania, Italy in 2024.
Dr Zhang has worked in the fields of AI, control systems, and signal processing since mid-1990s. His current research interests include computational intelligence, machine learning, intelligent systems and control, biomedical signal processing, and neurocomputing. So far he has published four books, 11 book chapters, and around 200 peer-reviewed journal and conference papers in those areas.
Dr Zhang served as Chair of IFAC (International Federation of Automatic Control) Technical Committee on Human-Machine Systems (2017-2023) and Vice Chair of IEEE Norway Section (2019- 2023). He currently serves as Vice Chair of IFAC Technical Committee on Human-Machine Systems (2023-) and Vice Chair of IEEE CIS (Computational Intelligence Society) Norway Chapter (2019-). He is on editorial board of four international journals, including Frontiers in Neuroscience, Cognitive Neurodynamics (Springer), and Cognition, Technology & Work (Springer). In addition, he was invited to serve as keynote speaker or chair for a number of international conferences.
Dr Zhang was listed in Stanford/Elsevier's World Top 2% Scientists Rankings in 2023 and 2024.

Speech Title: Stock Price Forecasting by Means of Transformer-based Ensemble Learning

Abstract: In this talk, for the stock price forecasting problem we compare the performance of several models, including traditional time series analysis model - ARIMA and four machine learning (ML) models (Linear Regression, Long Short-Term Memory (LSTM) network, Prophet, and Transformers). Ensemble learning is proposed to reduce the prediction biases and variances of those individual models. Furthermore, in order to handle the complexity and volatility of real-world stock markets, three different hyperparameters (such as learning rate, number of layers in the network model, batch size, etc.) tuning approaches (grid search, random search, and Bayesian optimization) are compared in terms of prediction accuracy and computational cost. The real stock data analysis results showed that ensemble learning method can improve accuracy and reliability of stock time series forecasting and that the transformer model stacked with linear regression achieved the best prediction performance. The results obtained may provide insights into stock closing price dynamics modeling, stock investment decision, and portfolio management.

Prof. Sami Brandt, IT-University of Copenhagen, Denmark

Prof. Sami Brandt got his doctoral degree in 2002 in Helsinki University of Technology, Finland, on the geometric branch of computer vision applied to electron tomography. After the doctoral degree he worked for one year as a research scientist in Instrumentarium Corporation Imaging Division, Finland, a couple of years in Helsinki University of Technology, Oulu University, Finland, and Malmö University, Sweden, and Nordic Bioscience Imaging/Synarc Imaging Technologies in Denmark. He currently work as associate professor in the Image Group in University of Copenhagen, Denmark. He have been a member of the IEEE, member of the Pattern Recognition Society of Finland, member of the International Association for Pattern Recognition (IAPR), and member of the Finnish Inverse Problems Society.

Speech Title: On the non-rigid structure-from-motion problem: from independent subspace analysis, degenerate basis shapes, and tensor-based factorisation to generative adversarial networks

Abstract: This talk provides an overview of our work on the non-rigid structure and motion problem with the application of the modeling and analysis of human faces. We start by presenting the classic formulation of the problem where the goal is to estimate the non-rigid affine structure and motion from 2D point correspondences and note its known difficulties and approaches taken to tackle them. Thereafter we show how independent subspace analysis can help to achieve a solution where no prior formation, apart from the assumption of statistical independence of the basis shapes, nor camera calibration information is required. Thereafter we develop another solution to the problem, by modifying the common assumption that the non-rigid shape is a linear combination of basis shapes, by adding an additional constraint, that the basis shapes should be degenerate. By this assumption, it is then possible to derive a solution that avoids the central problems of the classic problem setting. We likewise show, how tensor based modelling of faces and non-rigid structure-motion-problem can be unified into single tensor-based modelling problem. In the last part of the talk, we show how our non-rigid structure-from-motion approaches can be extended to generative models such as StyleGAN model to achieve factorization of latent manifolds into camera geometry, pose, and non-rigid structure that opens the way of photorealistic modelling, analysis and editing of human faces and the underlying geometry via the trained generator. The future directions are also discussed.