8th workshop in Advances in High Dimensional Big Data
DEC15-18, 2023, Sorrento, Italy
High dimensionality is inherent in applications involving text, audio, images and video as well as in many biomedical applications involving high-throughput data. Many applications involving relational or network data also produce massive high-dimensional data sets. To deal with the challenges in processing and analysing such data sets, a wide range of approaches are available. These include "large p, small n" settings, dimensionality reduction, clustering, manifold learning, random projections and etc. Such approaches are crucial in dealing with issues concerning statistical reliability, revealing and visualizing structure hidden by the high dimensionality and noise, as well as saving the computation and storage burden. The purpose of this workshop is two-fold: first to highlight novel research addressing high dimensionality and at the same time bringing in contact prominent researchers and practitioners in the particular aspect of big data analysis. The dual keynote talks from both the academia and the industry emphasizes the importance of bridging the gap between state-of-the-art research and practical applications. The workshop's interests range from applications involving high dimensional data to the theoretical aspects of the problem. In addition, there is a particular interest in techniques that take advantage of data-parallel/graph-parallel platforms to effectively handle truly large-scale real-world problems, and techniques that improve memory efficiency, a premium in streaming and distributed environments.
Research topics included in the workshop
The topics of this workshop include, but are not limited to:
Deep Learning for High-dimensional Data.
Small n / Large p problems
"Large p, small n" settings
Supervised/unsupervised/semi-supervised dimensionality reduction
Large-scale network analysis
Data clustering
Random projections for big data
High-dimensional data streams
Manifold learning for big data
Kernel-based approaches for big data
Non-negative matrix factorization for big data
Big data applications involving high dimensionality
Chaired by
Aristidis Vrahatis, Assistant Professor, Ionian University, Greece
Sotiris Tasoulis, Assistant Professor, University of Thessaly, Greece.
Nicos Pavlidis, Senior Lecturer, Lancaster University, UK.
Program Committee Members (recent and current)
Jussi Kangasharju, Professor, University of Helsinki, Finland.
Paulo Lisboa, Professor, Liverpool John Moores University, UK.
Zhirong Yang, Research fellow, HIIT, University of Helsinki, Finland.
Jon Crowcroft, Professor, University of Cambridge, UK.
Teemu Roos, Associate Professor, HIIT, University of Helsinki, Finland.
Daniel Lawson, Research Fellow, University of Bristol, UK.
Angelos Marnerides, Lecturer, Lancaster University, UK.
Richard Samworth, Professor, University of Cambridge, UK.
David Hofmeyr, Assistant Professor, Stellenbosch University, South Africa.
Michael Epitropakis, Lecturer, Lancaster University, UK.
Liang Wang, Senior Research Associate, University of Cambridge, UK.
Ioannis Anagnostopoulos, Associate Professor, University of Thessaly, Greece.
Sandra Ortega-Martorell, Lecturer, Liverpool John Moores University, UK.
Luca Martino, Assistant Professor, Universidad Carlos III de Madrid , Spain.
Ilias Maglogiannis, Associate Professor, University of Piraeus, Greece.
Michael Mathioudakis, Assistant Professor, University of Helsinki, Finland.
Rohit Babbar, Assistant Professor, Aalto University, Finland.
Panagiotis Vlamos, Professor, Ionian University, Greece.
Organizing Institutions
IEEE BigData Conference
See the Conference Website for details about the conference.
Previous Workshop Websites
Advances in High Dimensional Big Data (7st Workshop)
Advances in High Dimensional Big Data (6th Workshop)
Advances in High Dimensional Big Data (5th Workshop)
Advances in High Dimensional Big Data (4th Workshop)
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