EPSRC funded Workshop on “High ­Dimensional Big Data Engineering”

Friday 22 January 2016, Computer Laboratory, FW11, University of Cambridge.

Thanks to advances in monitoring devices and modelling techniques, modern big data grows in both its quantity and dimensionality. Many popular applications involve processing and understanding the information contained in high-dimensional datasets, for example, document classification, pattern recognition, intrusion detection, recommender systems, etc. The intelligence of these applications heavily relies on the efficacy of processing and extracting meaningful patterns out of the datasets and the accuracy of searching. In reality, the balance between efficiency and accuracy plays a key role in building scalable services.

The workshop, to be held at the Computer Laboratory, University of Cambridge on Friday 22 January 2016, aims to bring together the experts in computer science, statistics and mathematics in the leading institutes within and beyond the UK. It will focus on the state-of-the-art engineering and algorithmic solutions adopted in realistic and large-scale applications in the context of high-dimensional big data.

We invite all researchers interested in high-dimensional big data to participate in the workshop. Attendance at the workshop is free of charge and food will be provided. Please inform us if you have any dietary requirements. Please use the following link to register


Presentations and speakers

Large-Volume, High-Dimensional Data Processing at Thomson Reuters
Dr. Jochen Leidner (Director), Corporate Research & Development, Thomson Reuters, UK

Random Projection Ensemble Classification
Prof. Richard Samworth, Statistical Laboratory, University of Cambridge, UK

Fast Nearest Neighbor Search in High Dimensions by Multiple Random Projection Trees
Prof. Teemu Roos, Department of Computer Science, University of Helsinki, Finland

High-Dimensional Big Data Analysis
Dr. Dimitris Tasoulis (Senior Execution Researcher), Winton Capital Management, UK

A Knowledge Graph for Education and Learning
Mads Holmen (CEO), Bibblio Inc., UK

Multi Scale Machine Learning Methodologies for Molecular Biology Data
Dr. Pietro Lio’, Computer Laboratory, University of Cambridge, UK

Statistical Calculations at Scale Using Decisions and Emulation
Dr. Daniel Lawson, School of Social and Community Medicine, University of Bristol, UK

Uncovering Multi-Modal Spread Modes using Joint Diagonalisation
Dr. Eiko Yoneki, Computer Laboratory, University of Cambridge, UK

Optimal Hyperplanes for Clustering. Early results from High Dimensional Genomics Data
Dr. David Hofmeyr, Lancaster University

Accurate estimation of breakouts in high-dimensional panel data
Leonid Torgovitski Mathematical Institute, University of Cologne, Germany


Workshop Organisers
  • Dr. Liang Wang, University of Cambridge
  • Dr. Sotiris Tasoulis, Liverpool John Moores University
  • Prof. Jon Crowcroft, University of Cambridge