Advances in Data Science Conference 2017

Data Science Conference 850x486

More than 150 delegates from across Europe attended the 'Advances in Data Science Conference on 15th and 16th May at the Museum of Science and Industry. The conference was organized and co-sponsored by the Data Science Institute which acts as the gateway to The University of Manchester’s data science activity and research.

Over the course of two days, leading academic and industry data scientists took to the stage to present recent developments in data science and to discuss subjects such as diverse applications, software, privacy and advanced analytics (machine learning, Bayesian statistics and scalable algorithms.) Attendees had the opportunity to submit projects and to showcase their work through oral presentations and poster displays covering topics such as advances in machine learning and computational statistics, software, data science applications, privacy, and visualisation. The list of poster presentations is available here.

A range of excellent presentations came from organisations such as BBC, EPFL, Met Office, HESA to name but a few and speakers invited audience members to take part in lively Q & A discussions after each presentation. Many thanks to our brilliant speakers for their excellent presentations:

Day 1- Analytics & Privacy

  • Mark Girolami (Imperial College London and The Alan Turing Institute) Beyond Urban Analytics: Stochastic Differential Equations provide Insights to Retail Development in Cities.
  • Raia Hadsell (DeepMind) Overcoming Catastrophic Forgetting in Neural Nets
  • Tammo Rukat, (University of Oxford) Bayesian Boolean Matrix Factorisation
  • Frank Dondelinger, (Lancaster University) A Bayesian Model for Drug Response Estimation and Biomarker Testing using Gaussian Processes
  • Zoubin Ghahramani, (University of Cambridge and Uber AI) Automating Machine Learning
  • Yves-Alexandre de Montjoye, (Imperial College London) Computational Privacy: The privacy bounds of human behaviour
  • Borja de Balle Pigem, (Amazon) Secure Multi-Party Linear Regression on High-Dimensional Data
  • Natalie Shlomo, (University of Manchester) Confidentiality and Differential Privacy in the Dissemination of Frequency Tables
  • Michael Smith (University of Sheffield) Differentially Private Gaussian Processes
  • Jean-Pierre Hubaux, (EPFL) The Security and Privacy Challenges Raised by Precision Medicine
  • StJohn Deakins, (CitizenMe) Ethical Personal Data: Do Humans and Data Mix?

Day 2- Analytics, Visualisation, Software and Applications

  • Ruth King, (University of Edinburgh) Efficient semi-complete data likelihood approaches (and making the most out of the BUGS/JAGS black-box)
  • Gaël Varoquaux, (INRIA) Enabling open science and data science via software: scikit-learn
  • Neil Richards, (Higher Eductation Statistics Authority) Telling stories with data
  • Caroline Jay, (University of Manchester) Beyond conscious thought: using data about perception to understand cognition
  • Graham McNeill (Oxford Internet Institute, University of Oxford) Automatic Generation of Tile Maps
  • Timothy Cowlishaw (BBC Research & Development) Understanding culture with Data Science
  • Idris Eckley, (Lancaster University) Changepoint challenges: making sense of industrial sensor data
  • David van Dyk, (Imperial College London) Data-Driven and Science-Driven Statistical Methods in Astronomy and Solar Physics
  • Weisi Guo (University of Warwick and The Alan Turing Institute Data Centric Engineering Programme) Scalable and Adaptable Network Resilience
  • Niall Robinson, (Met Office Informatics Lab) Cloudbursting: analysing massive weather data in the cloud

The Data Science Institute would like to thank all the speakers and attendees for creating and contributing to such a successful and extremely insightful event. Special thanks go to Professor Magnus Rattray (University of Manchester & Data Science Institute) and Professor Neil Lawrence (Amazon) for hosting the conference and coordinating the Q & A sessions. 

For further information, please visit the Data Science Institute website or contact