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. I am also part of the ELLIS PhD program. My main research goal is to develop ML solutions for decision support and treatment recommendation systems, with an interest for offline reinforcement learning, representation learning and causal inference.

In 2023, I was a student researcher at Google, developing new methods in Reinforcement Learning from Human Feedback (RLHF) with Aliaksei Severyn and the wider Bard team.

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 and software engineering at CERN. I studied Physics, Materials Science and Machine Learning at Cambridge, where I consistently ranked first in my year.

I am actively looking for excellent Masters' students to supervise for their thesis (preferably at ETH). Reach out if our interests overlap!

  • 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»

[Jan 16, 2024] My paper on Delphic Offline RL is accepted to ICLR 2024.

[Dec 10, 2023] My paper on Embeddings for Clinical Time-Series is accepted to ML4H 2023.

[July 28, 2023] I presented my paper on Delphic Offline RL at 3 ICML workshops and at EWRL (including an oral!).

[May 30, 2023] I will be spending six months as a Student Researcher at Google Zürich, working with Aliaksei Severyn on RL methods to improve LLM training. Super excited!

[April 24, 2023] My paper on Temporal Label Smoothing is accepted to ICML 2023. See you in Hawaii 🌺🏝

Professional Experience

Student Researcher
May 2023 – Nov 2023 Zürich, Switzerland
Developed new methods in Reinforcement Learning from Human Feedback (RLHF) applied to Large Language Models (LLMs), within the Bard Model Quality team led by Aliaksei Severyn. Contributed to a new finetuning method based on human feedback, giving significant quality gains and integrated within model development. Working towards a patent and a publication at a major ML conference.
Doctoral Research Fellow
Sep 2021 – Present Zürich, Switzerland
My main research goal is to develop ML solutions for decision support and treatment recommendation systems. General areas of interest include offline reinforcement learning, representation learning, causal inference and time-series modelling.
Research Assistant - Machine 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 (1 and 2).
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. Resulted in three patent applications (1, 2 and 3)
Software Development Intern
Aug 2016 – Aug 2016 Geneva, Switzerland
Development of Invenio user interface and of demos for a reproducible analysis platform.


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

(2024). Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding. ICLR 2024.

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(2023). On the Importance of Step-wise Embeddings for Heterogeneous Clinical Time-Series. In ML4H 2023 (PMLR).

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(2023). Temporal Label Smoothing for Early Event Prediction. In ICML 2023.

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(2023). Clinical Trajectory Representations for Clustering. In ICLR TSRL4H 2023.

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

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