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Harrison Wilde

NOTICE: I will be working at Twitter's Cortex research lab from July to September 2021, and thus will be pausing my studies and some of my other responsibilities listed below for this period.

I am a second year PhD student in Statistics supervised primarily by Sebastian Vollmer at Warwick. I am also the 2019 recipient of the Feuer International Scholarship in Artificial Intelligence as well as a 2019 Data Science for Social Good Fellow and continue to support the fellowship and foundation as one of the principal organisers, reviewers and mentors for the UK programme.

My research interests include Bayesian and machine learning theory and methodology in constrained environments with applications in epidemiology, medicine and public health, as well as synthetic data generation, fairness and privacy across these contexts.

I mainly work with collaborators across the University of Oxford, UCL, The Wellcome Trust and The Alan Turing Institute, as a research scientist at Pumas AI, a visiting member of Chris Holmes' lab at Oxford, an honorary researcher at UCL Institute of Health Informatics, a visiting researcher at the British Heart Foundation's Data Science Centre via HDR UK, a member of the CVD-COVID-UK Consortium, a researcher at COEXI(S)T, a contributor to the Turing PPL and a visiting researcher at Homeless Link. I also currently sit on the committee of the Royal Statistical Society's West Midlands Local Group.

Outside of academia I am an enthusiastic pianist, composer and producer, and enjoy cooking, mountain biking and hiking.

Selected Publications

  • Sahra Ghalebikesabi, Harrison Wilde, Jack Jewson, Arnaud Doucet, Sebastian J. Vollmer, Chris Holmes. "Bias Mitigated Learning from Differentially Private Synthetic Data: A Cautionary Tale" arXiv (2021).
  • Harrison Wilde, John M. Dennis, Sebastian J. Vollmer, Bilal A. Mateen. "A national retrospective cohort study of serious operational problems reported by English hospital administrations and their association with intensive care mortality" medRxiv (2021).
  • Harrison Wilde, Thomas Mellan, Iwona Hawryluk, John M. Dennis, Spiros Denaxas, Christina Pagel, Andrew Duncan, Samir Bhatt, Seth Flaxman, Bilal A. Mateen, Sebastian J. Vollmer. “The association between mechanical ventilator availability and mortality risk in intensive care patients with COVID-19: A national retrospective cohort study” medRxiv (2020).
    • Was interviewed on BBC Evening News and featured in a lot of international media coverage, see Altmetric.
  • Gergo Bohner, Gaurav Venkataraman, Bilal A. Mateen, Harrison Wilde, Christopher Rackauckas, Andrew Duncan, Sebastian Vollmer. "Epidemiological modeling under uncertainty - lessons from the ongoing COVID-19 pandemic". arXiv (2020).
  • Harrison Wilde, Jack Jewson, Sebastian Vollmer, Chris Holmes. "Foundations of Bayesian Learning from Synthetic Data". AISTATS 2021; arXiv (2020).
  • Bilal A. Mateen, Harrison Wilde, John M. Dennis, Andrew Duncan, Nicholas John Meyrick Thomas, Andrew P. McGovern, Spiros Denaxas, Matt J. Keeling, and Sebastian J. Vollmer. "A geotemporal survey of hospital bed saturation across England during the first wave of the COVID-19 Pandemic." BMJ Open; medRxiv (2020).
  • Gergo Bohner, Gaurav Venkataraman, Harrison Wilde, Bilal A. Mateen and Sebastian J. Vollmer. "Modelling COVID-19 exit strategies for policy makers in the United Kingdom." Independent (2020).

  • Harrison Wilde, Lucia Lushi Chen, Austin Nguyen, Zoe Kimpel, Joshua Sidgwick, Adolfo De Unanue, Davide Veronese, Bilal A. Mateen, Rayid Ghani, and Sebastian J. Vollmer. "A Recommendation and Risk Classification System for Connecting Rough Sleepers to Essential Outreach Services." Cambridge Data & Policy, Vol. 3; arXiv (2020).


  • 2020 - 2021
    • ST346: Generalised Linear Models for Regression and Classification
    • ST343: Topics in Data Science
    • ST104: Statistical Laboratory
    • CS118: Programming for Computer Scientists
  • 2019 - 2020
    • ST343: Topics in Data Science
    • CS910: Foundations of Data Analytics
    • CS118: Programming for Computer Scientists
    • IB9CS: Big Data Analytics