Julia A. Meister

Research Student, MSc, BSc


Research activity per year

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Personal profile

Scholarly biography


  • Nov 2022 -
    • Won the Best Poster Award at the "Machine Learning for Healthcare" conference, organised by the Institute of Physics in London.
    • Senior Research Support Assistant, developing a network science approach to dynamically store unstructured social media data with Knowledge Graphs.
  • Aug 2022 -
    • Published the proceedings paper "Cough-based COVID-19 detection with audio quality clustering and confidence measure based learning". Presented at the COPA2022 conference, where it won the Best Student Paper Award.
    • Published the proceedings paper "Audio feature ranking for sound-based COVID-19 patient detection", presented at the EPIA2022 conference.
  • July 2022 - Published the proceedings paper "A novel cough audio segmentation framework for COVID-19 detection", presented at the ODAK2022 conference.
  • June 2022 - Winner of the "3 Minute Thesis" (3MT) competition at the University of Brighton, progressing to the UK national competition.
  • Apr 2022 - Research Officer, developed novel uncertainty quantification methods for Deep Transfer Learning.
  • Feb 2022 - Research Officer, evaluated the carbon footprint of remanufactured catheters. Collaborated closely with the NHS and two US-based medical device remanufacturers: AMDR and Innovative Health.
  • Oct 2021 - Presented in-progress work on Machine Learning COVID-19 cough detection at the IRSH2021 conference.
  • July 2021 - Supervised and supported a Brighton undergraduate student's research activities as part of the Santander-funded "Global Challenges" grant.
  • Apr 2021 - 1 of 7 winners of the Santander-funded "Global Challenges" grant, in collaboration with Dr Khuong Nguyen, Prof Zhiyuan Luo, and Cardisio (Germany), an industry practitioner specialising in Machine Learning for healthcare.
  • Jan 2021 - Joined the University of Brighton as a PhD student.
  • Dec 2020 - Awarded MSc in Data Science and Analytics, thesis titled "Conformal Predictors for detecting harmful respiratory events".


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 uncertainty quantification and Confidence Machine Learning methods for Digital Health.

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 uncertainty quantification, ML's application in the safety-critical 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.

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

Education/Academic qualification

Master, Royal Holloway University of London

Sep 2019Dec 2020

Bachelor, Royal Holloway University of London

Sep 2016Jun 2019


  • QA75 Electronic computers. Computer science
  • Digital Health
  • Confidence Machine Learning
  • Data Science and Analytics


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