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December 9-12, 2019, Los Angeles, CA, USA
December 9-12, 2019, Los Angeles, CA, USA
Announcement: Due date for workshop papers submission extended to Oct. 21, 2019
Workshop in conjunction with the 2019 IEEE International Conference on Big Data (IEEE Big Data 2019)
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
Research topics included in the workshop
The topics of this workshop include, but are not limited to:
- "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
Invited keynote speakers
Invited keynote speakers
Academic Keynote Speaker: TBA
Industry Keynote Speaker: TBA
Previous keynote speakers:
- Ben Snively, AI/ML Principal Solution Architect, Big Data and Analytics, Amazon Web Services (AWS).
- Bin Yu, Chancellor's Professor, Department of Statistics, Department of Electrical Engineering & Computer Science, UC Berkeley, USA.
- Dimitris Tasoulis, Senior Execution, Researcher, Winton Capital Management, UK.
- Tony Jebara, Netflix and Columbia University, USA.
- Maxime Fournes, Data Scientist and Machine Learning Engineer, Seldon Technologies , UK.
Chaired by
Chaired by
- Sotiris Tasoulis, Assistant Professor, University of Thessaly, Greece.
- Nicos Pavlidis, Senior Lecturer, Lancaster University, UK.
- Teemu Roos, Associate Professor, HIIT, University of Helsinki, Finland.
Program Committee Members (recent and current)
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.
- Aristidis Vrahatis, Research fellow, University of Thessaly, Greece.
Organizing Institutions
Organizing Institutions
IEEE BigData Conference
IEEE BigData Conference
See the Conference Website for details about the conference.
Previous Workshop Websites
Previous Workshop Websites
Advances in High Dimensional Big Data (1st Workshop)
Sponsored by
Sponsored by
TBA
previously sponsored by:
previously sponsored by:
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