Luis Martinez

Abstract: to follow shortly..

Bio-sketch: LUIS MARTíNEZ is Full Professor at the University of Jaén, Jaén, Spain.  He  has been main researcher in 19 R&D projects, also has published more than 350  JCR papers and more than 300 contributions in Inter/national Conferences related to his areas. His current research interests include intelligent decision support, computational intelligence, trustworthy AI, Distributed Ledger Technologies. He was a recipient of the IEEE Transactions on fuzzy systems Outstanding Paper Award 2008 and 2012. He is Co-Editor-in-Chief of the International Journal of Computational Intelligence Systems. He is IFSA Fellow 2021.  Eventually, he has been appointed as Highly Cited Researcher 2017-2024 in Computer sciences.

Sung-Bae Cho, Underwood Distinguished Professor, Yonsei University, South Korea

Abstract: to follow shortly..

Bio-sketch: Sung-Bae Cho received Ph.D. degree in computer science from KAIST (Korea Advanced Institute of Science and Technology). He was an invited researcher of Human Information Processing research laboratories at ATR (Advanced Telecommunications Research) institute, Japan, from 1993 to 1995, a visiting scholar at University of New South Wales, Australia, in 1998, a visiting professor at University of British Columbia, Canada, from 2005 to 2006, and at King Mongkut’s University of Technology at Thonburi, Thailand, in 2013. Since 1995, he has been a professor in department of computer science, Yonsei University, a Underwood distinguished professor from 2021, and a Yonsei Fellow from 2023. His research interests include neural networks, pattern recognition, intelligent man-machine interfaces, evolutionary computation, and artificial life. Dr. Cho was the recipient of the Richard E. Merwin prize from IEEE Computer Society in 1993. He received several distinguished investigator awards from Korea Information Science Society in 2005, and Gaheon Sindoricoh in 2017. He is also a recipient of service merit medal from Korean government in 2022, and , and Sudang prize in 2026. Currently he is the Fellow of IEEE, AAIA, KAST (Korean Academy of Science and Technology), and NAEK (National Academy of Engineering of Korea).

John A. Lee, Professor and FNRS Research Director, Université catholique de Louvain, Belgium

Abstract: Dimensionality reduction (DR) aims representing high-dimensional data in lower-dimensional spaces, often with just two dimensions, for visualization purposes. Historically, DR has primarily faced the challenge of high dimensionality (high D), trying to overcome or at least mitigate the so-called curse of dimensionality. More recently, over the last decade mostly, a second challenge arose: scalability not only for high D but also big N, namely, the size of the data set to embed. This second challenge is especially tough, as a majority of DR methods consider pairwise data features that are compared across both high- and low-dimensional spaces, like dissimilarities or similarities. Therefore, the baseline computational complexity of DR is quadratic, if not cubic, when Gram matrices get eigen-decomposed, such as in spectral embedding. An overview of the state of the art will show the successive approaches to decrease the computational complexity with different approximations and data structures. Such approaches include various forms of subsampling with vector quantization, landmarks, or space-partitioning trees. A particular focus is dedicated to methods of neighbor embedding (NE) like t-SNE and UMAP, which have popularized these approaches (Barnes-Hut trees, multipole expansion, negative sampling), often inspired from accelerated solvers of N-body placement problems in physics. Open questions remain, like the sweet spot between computational efficiency and trustworthiness, considering that speed often stems from sparsity, with (dis)similarities restricted mostly to a few close neighbors (K<<N). Practitioners of large-scale DR in computational biology have raised the issue of how faithfully NE can preserve the global geometry of data. Complementary aspects like scalability of multidimensional scaling (MDS) and self-organizing maps (SOMs), as well as scalable quality assessment of DR will be briefly dealt with too.

Bio-sketch: John Aldo Lee was born in 1976 in Brussels, Belgium. He received the M.Sc. degree in Applied Sciences (Computer Engineering) in 1999 and the Ph.D. degree in Applied Sciences (Machine Learning) in 2003, both from the Université catholique de Louvain (UCLouvain, Louvain-la-Neuve, Belgium). He is currently a Research Director with the Belgian fund of scientific research (Fonds de la Recherche Scientifique, F.R.S.-FNRS).

During several postdocs, he developed specific image enhancement techniques for positron emission tomography in the Centre for Molecular Imaging and Experimental Radiotherapy of the Saint-Luc University Hospital (Belgium). In parallel, he is also an active member of the UCL Machine Learning Group, with research interests focusing on data visualization, dimensionality reduction, intrinsic dimensionality estimation, clustering, and image processing.