Emma Rocheteau

Emma Rocheteau

ML for Healthcare MB/PhD Student

University of Cambridge


I am an MB/PhD student at the University of Cambridge working on machine learning problems for healthcare. During my PhD (2017-2021) I was part of the AI group in the computer science and technology department. I am now a final year medical student and I will become an (academic) foundation doctor in West Suffolk and Addenbrooke’s Hospitals from next year.

I have previously intercalated in part IIA engineering.

Download my CV.

  • Deep Learning
  • Graph Neural Networks
  • Electronic Health Records
  • Healthcare
  • Medicine
  • MB BChir (in progress), 2023

    University of Cambridge

  • PhD in Deep Learning, 2023

    University of Cambridge

  • BA in Engineering/Preclinical Medicine, 2016

    University of Cambridge

Research Interests

My work has focused on predicting patient outcomes in the Intensive Care Unit (ICU). When designing my deep learning models, I am often inspired by my knowledge of clinical decision making.

For example, for time series processing in Electronic Health Records (EHRs), I use temporal and pointwise convolution to efficiently extract patient trajectories over time – a method inspired by clinicians.

I am also working on using graph neural networks to link the experiences of similar patients. The rationale is that when clinicians make decisions they will typically lean on their past experience, especially if they are dealing with a rare disease.

If you’re interested in the kind of stuff I do, follow me on twitter!

Other Papers

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(2020). Deep Transfer Learning for Automated Diagnosis of Skin Lesions from Photographs. In ML4MH at NeurIPS 2020.

PDF Cite Poster arXiv

(2020). Forecasting Ultra-early Intensive Care Strain from COVID-19 in England. In medRxiv.

PDF Cite Code Poster medRxiv


Academic Director
Aug 2020 – Present
Institute dedicated to advancing patient-centered care with medical AI.
Researcher Intern
Jul 2019 – Oct 2019 Cambridge, UK
13-week internship with the healthcare intelligence team investigating length of stay and discharge location prediction in the ICU.
Data Science Intern
Jul 2018 – Sep 2018 Cambridge
8-week internship involving feature engineering mouse/keyboard data to identify financial fraud using a random forest classifier.
Physiology Supervisor
Jan 2017 – Present Cambridge
I teach 11 first year undergraduate medical students cardiovascular, respiratory, renal, digestion, endocrinology and nerve and muscle electrophysiology. My role as supervisor involves tutorials each week, plus marking work (mainly essays) and answering any questions the students may have.

Reviewing Experience

  • Machine Learning for Healthcare Conference (MLHC) 2021
  • Machine Learning for Preventing and Combating Pandemics (MLPCP) Workshop at ICLR 2021
  • Trustworthy AI for Healthcare (TAIH) Workshop at AAAI 2021
  • AMIA 2021 Informatics Summit
  • Machine Learning for Health (ML4H) Workshop at NeurIPS 2020*
  • Graph Representation Learning and Beyond (GRL+) Workshop at ICML 2020
  • Graph Representation Learning (GRL) Workshop at NeurIPS 2019
  • Machine Learning for Health (ML4H) Workshop at NeurIPS 2019

*My ML4H reviews were explicitly recognised as excellent by metareviewers


  • Eliot Slater Prize in Psychiatry, University of Cambridge, 2022
  • Finalist in the CSAR Awards, for the societal impact of my PhD, 2022
  • 2nd Runner-Up for Best Short Paper Award, W3PHIAI Workshop at AAAI 2021
  • Best Talk Prize, Oxbridge Women in Computer Science Conference 2020
  • Best Poster Prize (49 submissions), 2nd HBP Student Conference 2018
  • AC Comfort Essay Prize, Royal Society of Medicine, 2017
  • Cambridge Medical Journal Essay Competition, 1st Prize, 2017
  • 3rd Year Computer-Based Project Prize, Department of Engineering, 2016
  • Gold Rob Clarke Award for Undergraduate Research in Physiology, 2016
  • Churchill College Medical and Veterinary Prize, 2014, 2015 & 2016
  • Gold Award in the British Biology Olympiad, Royal Society of Biology, 2013
  • Gold Award in the British Chemistry Olympiad, Royal Society of Chemistry, 2013
  • Gold Award (& top 100) in the British Physics Olympiad, University of Oxford, 2013