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Research interests

Given a Machine Learning prediction, how confident are you that it is correct?

Often, we assume that models will be about as successful on future samples as they are on their training data. Without a guarantee of prediction quality, ML's application in a healthcare setting can be unreliable.

In contrast, Confidence ML associates individual predictions with confidence levels to provide guaranteed error probabilities per sample. My research centres on developing and improving ML techniques to provide confident and efficient predictions for Digital Health applications.

Recent projects include detecting COVID-19 infections from respiratory audio, and identifying the risk of coronary heart disease from EKG sensor data.

Keywords: Digital Health • Confidence Machine Learning • Data Science and Analytics

Scholarly biography

After completing BSc and MSc degrees at Royal Holloway, University of London, I joined Brighton University's School of Computing, Engineering & Maths. Under the supervision of Dr Khuong Nguyen, Dr Marcus Winter, and Prof Alison Bruce, I am pursuing a PhD focused on developing Confidence Machine Learning for Digital Health.


  • Apr 2021 - 1 of 7 winners of the Santander-funded Global Challenges grant, in collaboration with Dr Khuong Nguyen, Prof Zhiyuan Luo, and Cardisio.
  • Jan 2021 - Joined Brighton University as a Computing PhD student.
  • Dec 2020 - Awarded MSc in Data Science and Analytics, thesis titled Conformal Predictors for detecting harmful respiratory events.

Education/Academic qualification

Master, Royal Holloway University of London

Sep 2019Dec 2020

Bachelor, Royal Holloway University of London

Sep 2016Jun 2019


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