Mathematics of Big Data: Lessons from COVID-19

Compass co-director Daniel Lawson is organising a one-off event on the “Mathematics of Big Data: Lessons from COVID-19” 

Abstract:

Generating a causal understanding of interventions for COVID-19 requires a confluence of several topics: statistical modelling and causal inference, estimation of mathematical models, and working with Big Data. This raises several difficult challenges that current mathematical approaches have gained some limited traction on. This event explores the mathematical challenges and opportunities that the COVID-19 epidemic has highlighted, that will go on to be important into the future.
The special format will include a discussion panel where our speakers and audience can speculate on the importance of different mathematical research directions for the handling of the COVID-19 crisis and problems that have a similar character of blending modelling with large scale statistical inference.

Invited Speakers and Talk Titles

Ritabrata Dutta (University of Warwick) – Optimal lockdown using Google mobility (joint work with Susana Gomes, Dante Kalise and Lorenzo Pacchiardi)
Ricardo Silva (University College London) – Some Thoughts of Computationally Intensive Causal Inference
Sam Tickle (University of Bristol) – Detecting Local and Universal Changes in Big Data: from Global Terrorism to COVID-19

Programme

12:00 – Introduction
12.10 – Ricardo Silva (University College London)
13.00 – Ritabrata Dutta (University of Warwick)
13.50 – Short Break
14.00- Sam Tickle (University of Bristol)
14.50 – Panel Discussion
15.30 – Finish

Organising Committee Chair – Daniel Lawson (Bristol)

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