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Department of Statistics

Hannah Craddock

I am a third year PhD student under the supervision of Dr Simon Spencer and Prof Xavier Didelot (2nd year of PhD proper following one year of a CDT). My research is focused on Bayesian inference applied to epidemic outbreaks. This involves the development of bespoke multi-parameter Markov-Chain Monte Carlo (MCMC) algorithms with a focus on inference of super-spreading events and super-spreading events individuals. Methods such as data augmentation, adaptive algorithms and reversible jump mcmc (RJMCMC) have been used in conjunction. I am currently fitting the models to data from the SARs outbreak of 2003. Given my background in data science, machine learning and AI in industry, I also have a keen interest in these areas.

Project Github

Research Themes

  • Bayesian inference applied to epidemic data
  • Multi-parameter Markov-Chain Monte Carlo (MCMC) algorithms
  • Super-spreading events and super-spreading individuals
  • Stochastic epidemic models
  • Model comparison - via reversible jump MCMC (rjMCMC) and Bayes factors
  • Model criticism - via posterior predictive checking
  • SARs Outbreak data
  • R Packages

Education

  • 2021-present: PhD, Mathematics and Statistics, University of Warwick
  • 2015-2017: MEng, Engineering (Biomedical), University College Dublin (EGA Gold medal awarded for 1st in class)
    • MEng Thesis project; EEG study investigating the neural mechanisms of decision-making. Involved a 20 subject EEG study, signal analysis and the development of a neural decision making model. Paper Pre-print
    • Modules; Statistical Data Mining, Optimization Techniques etc
  • 2012-2015: BSc, Engineering, University College Dublin

Work Experience

  • 2018 - 2019: Data scientist (junior) at Optum, United Health Group, Dublin
    • Data science position on a Research & Development data science team
    • Role involved AI model development in Python & PySpark on big data (electronic medical records & claims data). Projects included developing;
    • An AI model & Data Analysis pipeline to predict expensive members.
    • An AI model & Data Analysis pipeline to predict fraudulent claims. Neural network model developed. Sent to production team for real-time integration in Fraud Detection unit.
  • 2017 - 2018: Data Analyst at Accenture (Innovation Centre, Dublin)
    • Data Analyst on 'Analytics & AI' team at the Dock
    • Role involved AI and data analysis projects in Python including;
    • A Patented AI employee security project (patent). A novel AI model was developed to automate employee security access of an external client company across its global workforce. Final Patent used Frequent Pattern Mining based on my coded prototype
    • Fitbit project; Used ML to predict a user's sleep quality using Fitbit data
  • 2016: Applications Engineer, Shimmer sensors, Dublin (Masters Internship)
    • Application engineer on an Agile software engineering team at Shimmer wearables.

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