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Advances in Data Science 2018

More than 200 delegates came from around the world to Advances in Data Science 2018, a two-day international conference which will explore recent developments in data science and discuss data science’s potential to support societal well-being. The 2018 conference was jointly organised by The University of Manchester’s Data Science Institute and the Cathie Marsh Institute for Social Research.

Day 1

  • Jasmine Latham, Office for National Statistics, Data Science Campus Data science for public good – harnessing the power of data science at the Data Science Campus, Office for National Statistics
  • John Quinn, UN Global Pulse, Uganda – Humanitarian Applications of Machine Learning with Remote Sensing Data
  • Michael Smith, Joel Ssematimba, Mauricio A Alvarez and Engineer Bainomugisha –Gaussian Process Models for Low Cost Air Quality Monitoring
  • Reham Badawy, Yordan Raykov, Luc Evers, Bastiaan Bloem, Marjan Faber, Andong Zhan, Kasper Claes and Max Little –Automated quality control for sensor based symptom measurement performed outside the lab
  • Ciira wa Maina, Dedan Kimathi University of Technology, Kenya –Leveraging Machine Learning, Citizen Science and Low Cost Sensors for Acoustic Monitoring of Ecosystems: A Case Study in Kenya
  • Reka Solymosi, University of Manchester – Everybody lies but not everybody tweets: making sense of the bias in your data
  • Toby Davies, University College London –Understanding and predicting urban patterns of crime
  • Jonathan Carlton, Andy Brown, John Keane and Caroline Jay, University of Manchester – Using Low-Level Interaction Data to Explore User Behaviour in Object-Based Media Experiences
  • Pete Burnap, Cardiff University – Data Science for Cyber Security
  • Omar Costilla-Reyes, Patricia Scully and Krikor Ozanyan – Analysis of Spatio-temporal Representations for Robust Footstep Recognition with Deep Residual Neural Networks
  • Haiping Lu, University of Sheffield –Tensor Analysis and Learning for Multidimensional Data in Brain Imaging

Day 2

  • Licia Capra, University College London Offline biases in online platforms
  • Walid Magdy, University of Edinburgh – Online Users’ Behaviour Understanding and Prediction with Data Science
  • Jonathan Nagler, New York University, USA – Geo-Locating Twitters Users into Political Places
  • Niklas Loynes – Forecasting the 2018 US midterms: A social panel approach
  • Federico Botta, Tobias Preis and Helen Susannah Moat – Early indicators of the number of visitors to museums based on Google data
  • Ciro Cattuto, ISI Foundation, Italy – Demographics – Social Networks in Physical Space
  • Danielle Belgrave, Microsoft Research and Imperial College London –Machine Learning for personalised healthcare
  • Giovanni Mizzi, Tobias Preis, Leonardo Bastos, Marcelo Ferreira Da Costa Gomes, Claudia Torres Codeço and Helen Susannah Moat – Tracking dengue in Rio de Janeiro using Google and Twitter: an operationally realistic approach
  • Glen Martin, Mamas Mamas, Niels Peek, Iain Buchan and Matthew Sperrin –A Multiple-Model Generalisation of Updating Clinical Prediction Models
  • Elizabeth Buckingham-Jeffery, University of Manchester –Gaussian process approximations for fast inference from infectious disease data  
  • Philip Bourne, University of Virginia – Student team hacking into problem of veteran suicide


-Professor Magnus Rattray (Data Science Institute/ University of Manchester)

-Professor Rachel Gibson (The University of Manchester)

-Dr Suzy Moat (Warwick Business School and Alan Turing Institute)

-Neil Lawrence (Amazon Research)

-Professor Mark Elliot (The University of Manchester)