Advances in High Dimensional Big Data, Workshop in conjunction with the IEEE BigData Conference 2015, Santa Clara, CA, USA

Publish Date: Aug 19, 2015

Workshop in conjunction with the IEEE BigData Conference 2015

Oct 29 - Nov 1, 2015 @ Santa Clara, CA, USA

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 such problems, a wide range of approaches are available. These include "large p, small n" settings, dimensionality reduction, clustering, manifold learning, random projections, 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 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 emphasize the importance of building bridges 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 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:

- "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

Academic Keynote Speaker:

- Bin Yu, Chancellor's Professor, Department of Statistics, Department of Electrical Engineering & Computer Science, UC Berkeley, USA

Industry Keynote Speaker:

- Dimitris Tasoulis, Senior Execution Researcher, Winton Capital Management, UK

Chaired by

- Sotiris Tasoulis, Helsinki Institute for Information Technology HIIT, University of Helsinki, Finland

- Teemu Roos, Department of Computer Science, University of Helsinki, Finland

- Jukka Corander, Department of Mathematics and Statistics, University of Helsinki, Finland

Program Committee Members

  • Sivaraman Balakrishnan, Postdoctoral Researcher, UC Berkeley, USA 
  • Jussi Kangasharju, Professor, University of Helsinki, Finland 
  • Nicos Pavlidis, Lecturer, Lancaster University, UK 
  • Karl Rohe, Assistant Professor, University of Wisconsin-Madison, USA 
  • Narayana Prasad Santhanam, Assistant Professor, University of Hawaii at Manoa, USA 
  • Wojciech Szpankowski, Professor, Purdue University, USA 
  • Erik Aurell, Professor, KTH, Sweden 
  • Timo Koski, Professor, KTH, Sweden 
  • Zhirong Yang, Research fellow, HIIT, University of Helsinki, Finland 
  • Liang Wang, Research Associate, University of Cambridge, UK 
  • Vassilis Plagianakos, Associate Professor, University of Thessaly, Greece 
  • Ioannis Konstantinou, Senior Researcher, National Technical University of Athens, Greece 
  • Christoforos Anagnostopoulos, Lecturer, Imperial College, London, UK
  • Jon Crowcroft, Professor, University of Cambridge, UK

Further Official Information

Link to Original

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Host Countries

United States

Event Types