Alizée Pace

Alizée Pace

PhD Student in Machine Learning

ETH Zürich & MPI-IS

About Me

Hi! I’m Alizée, a PhD student in Machine Learning and a fellow at the ETH AI Center in Zürich. I am lucky to work with Prof. Gunnar Rätsch and Prof. Bernhard Schölkopf. My main research goal is to develop ML solutions for clinical decision support and personalised treatment recommendation, with an interest for offline reinforcement learning, representation learning and causal inference.

Before my PhD, I led a project on imitation learning for clinical decision-making with Prof. Mihaela van der Schaar at the University of Cambridge. My professional experience also includes medical device development for stroke treatment, sensor-assisted surgery and 3D-printed heart stents, as well as software engineering at CERN. In parallel, I studied Physics, Materials Science and Machine Learning at Cambridge, where I consistently ranked first in my year.

  • Offline RL & Imitation
  • Representation Learning
  • Causal Inference
  • Clinical Time-Series
  • PhD in Machine Learning, started 2021

    ETH Zürich

  • MPhil in Machine Learning and Machine Intelligence, 2021

    University of Cambridge

  • BA MSci in Materials Science, 2020

    University of Cambridge

Recent News

All news»

[Sept 1, 2022] I was featured in the latest NZZ Folio (major Swiss newspaper): These young people are building our future.

[Jan 28, 2022] My first first-author paper has been accepted to ICLR 2022 as spotlight (5% acceptance rate)!

[Sept 29, 2021] I have been awarded a Distinction for my Machine Learning MPhil at Cambridge (top 2-3 students in cohort).

[Sept 15, 2021] I have started my PhD at the ETH AI Center in Zürich.

Professional Experience

Doctoral Research Fellow
Sep 2021 – Present Zürich, Switzerland
My main research goal is to develop ML solutions for clinical decision support and personalised treatment recommendation in the intensive care unit. General areas of interest include offline reinforcement learning, representation learning, causal inference and time-series modelling.
Research Assistant - Clinical Imitation Learning
Mar 2021 – Sep 2021 Cambridge, UK
Research project on interpretable imitation learning for clinical decision support. Our goal was to describe and understand treatment or diagnostic policies through novel decision tree models, and capture how decision-making behaviour varies over time with patient information. Publication accepted as Spotlight for ICLR 2022 (5% acceptance rate).
Research Assistant - Bioelectronics
Sep 2019 – Mar 2020 Cambridge, UK
Research project on printed biocompatible force sensors for orthopaedic implants. Resulted in two publications (here, and here) and a poster presentation.
R&D Engineering Intern - Medical Device Data Analysis
Jul 2019 – Sep 2019 Barcelona, Spain
Development of a new thrombectomy device which restores blood flow in stroke patients. Patent application in process.
Research Assistant - 3D-Printing and CT Imaging
Jun 2018 – Aug 2018 Cambridge, UK
Optimised the 3D-printing technique for biodegradable heart stents.
Software Development Intern
Aug 2016 – Aug 2016 Geneva, Switzerland
Development of Invenio user interface and of demos for a reproducible analysis platform.
Programming Teaching Assistant
Jan 2016 – Sep 2016 Lausanne, Switzerland
Selected to write a 85-page book of lecture summaries for a C++ object-oriented programming course.


See my Google Scholar profile for a full list of publications.

(2022). Conformable and robust force sensors to enable precision joint replacement surgery. In Materials & Design.

Cite Source Document

(2022). POETREE: Interpretable Policy Learning with Adaptive Decision Trees. In ICLR 2022.

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(2021). Aerosol-jet-printed, conformable microfluidic force sensors. In Cell Reports Physical Science.

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(2020). Functionalised Capacitive Microfluidic Force Sensors for Orthopaedic Implants. In World Biomaterials Congress.

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