The winter school will consist of three days of lectures delivered by world leading experts in AI for health covering methods, tools, application, ethics and more. Following these three days, there will be two days of practical sessions linked with these lecture to give you hand-on experience with the material you will have learned.

Find out about the speakers and practical session leaders below.

Plenary speakers (Monday to Wednesday)

Sophia Ananiadou

Director, National Centre for Text Mining, UK
Turing Fellow and Chair in Computer Science, University of Manchester, UK

on "Biomedical Text Mining: methods, tools and applications"

Fields of expertise

Natural Language Processing, Text Mining


Michael Bronstein

Professor & Chair in Machine Learning and Pattern Recognition, Imperial College London
Head of Graph Learning Research, Twitter, UK

on "Geometric deep learning on graphs and manifolds: going beyond Euclidean data"

Fields of expertise

Michael Bronstein joined the Department of Computing as Professor in 2018. He has served as a professor at USI Lugano, Switzerland since 2010 and held visiting positions at Stanford, Harvard, MIT, TUM, and Tel Aviv University. His areas of expertise are Machine learning, Geometric deep learning, Graph representation learning, Computer vision and pattern recognition.


Dorin Comaniciu

Senior Vice President for AI and Digital Innovation at Siemens Healthineers, Princeton, New Jersey, USA

on "Artificial Intelligence for Healthcare: From Hype to Value"

Fields of expertise

Expert in technology development for diagnostic imaging and image-guided surgery
Medical imaging, machine learning, image-guided interventions, computer vision


Barbara Engelhardt

Associate Professor, at the Computer Science Department, Center for Statistics and Machine Learning, Princeton University, USA

on "Machine learning to improve clinical care: What exists, and what is left to do"

Fields of expertise

Barbara Engelhardt joined Princeton's Computer Science Department in 2014 as an assistant professor, and was promoted to associate with tenure in 2018. She was an assistant professor at Duke University before that. She has worked at NASA, Google Research, 23andMe, and Genomics plc. Her research focuses on statistical models and machine learning methods for the analysis of biomedical data including genomics, longitudinal studies, and clinical health.


Pearse Keane

Associate Professor at UCL Institute of Ophthalmology, UK
Consultant Surgeon at Moorfield’s Eye Hospital, UK

on "Transforming healthcare with artificial intelligence - lessons from ophthalmology"

Fields of expertise

Pearse Keane is a consultant ophthalmologist at Moorfields Eye Hospital, London and an associate professor at UCL Institute of Ophthalmology. He is originally from Ireland and received his medical degree from University College Dublin (UCD). He specialises in the treatment of retinal diseases such as age-related macular degeneration (AMD) and diabetic retinopathy. He leads a clinical research group which focuses on the development, evaluation, and implementation of artificial intelligence (AI) in healthcare.
In 2016, he initiated a formal collaboration between Moorfields Eye Hospital and Google DeepMind, with the aim of developing AI algorithms for the earlier detection and treatment of retinal disease. In August 2018, the first results of this collaboration were published in the journal, Nature Medicine. In October 2019, he was included on the Evening Standard Progress1000 list of most influential Londoners.


Susan Murphy

Professor of Statistics, Professor of Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences
Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University, USA

on "Challenges in Developing Online Learning and Experimentation Algorithms in Mobile Health"

Fields of expertise

Dr. Murphy’s lab develops experimental designs, including online algorithms, as well as data analysis methods to improve real time sequential decision-making in mobile health. In particular, her lab develops algorithms, deployed on wearable devices, to deliver and continually optimize individually tailored treatments. She developed the micro-randomized trial for use in constructing mobile health interventions; this trial design is in use across a broad range of health related areas. In these trials each participant can be randomized or re-randomized 100’s of times. Some examples of micro-randomized trials that are completed or are in the field can be found at https://methodology.psu.edu/ra/adap-inter/mrt-projects#proj. Dr. Murphy is a member of the National Academy of Sciences and of the National Academy of Medicine, both of the US National Academies. In 2013 she was awarded a MacArthur Fellowship for her work on experimental designs to inform sequential decision making. She was the 2019-2020 President of the Institute of Mathematical Statistics.


Adrian Weller

Programme Director for AI at the Alan Turing Institute, UK
Principal Research Fellow at the University of Cambridge, UK

on "Trustworthy AI"

Fields of expertise

Adrian Weller is Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI, where he is also a Turing Fellow leading work on safe and ethical AI. He is a Principal Research Fellow in Machine Learning at the University of Cambridge, and at the Leverhulme Centre for the Future of Intelligence where he leads the project on Trust and Transparency. His interests span AI, its commercial applications and helping to ensure beneficial outcomes for society. He serves on several boards including the Centre for Data Ethics and Innovation. Previously, Adrian held senior roles in finance.

Full day, in-depth, practical sessions (Thursday & Friday)

Ninon Burgos

PRARIE, 3IA Paris, France
Researcher, Paris Brain Institute, France

in a session on deep learning for medical imaging

Fields of expertise

I am a tenured research scientist (CR) at CNRS in the Paris Brain Institute. My research focuses on the processing and analysis of medical images, on the use of images to guide diagnosis, and on the application of these methods to the clinic. My current interests lie in the individual analysis of medical images to improve diagnosis in the context of neurodegenerative diseases.


Fenia Christopoulou

Post-doctoral Researcher at NaCTeM, University of Manchester, UK

in a session linked with the plenary lecture by Sophia Ananiadou demonstrating the National Centre for Text Mining (NaCTeM, UK) tools

Fields of expertise

Intra and Inter-sentence Relation Extraction with Deep Neural Networks


Walter Dempsey

Assistant Professor, School of Public Health, University of Michigan, USA

in a session on learning algorithms for improving and personalizing real-time treatment policies in mobile heath linked with Susan Murphy's plenary lecture

Fields of expertise

My research focuses on Statistical Methods for Digital and Mobile Health. My current work involves three complementary research themes: (1) experimental design and data analytic methods to inform multi-stage decision making in health; (2) statistical modeling of complex longitudinal and survival data; and (3) statistical modeling of complex relational structures such as interaction networks;


Alexandre Gramfort

Inria, Université Paris-Saclay, France

Machine learning on electrophysiology EEG signals

Fields of expertise

Alexandre Gramfort is a senior researcher at INRIA (DR), France. He was formerly Assistant Professor at Telecom Paris, Université Paris-Saclay, in the image and signal processing department from 2012 to 2017. He is also affiliated with the Neurospin imaging center at CEA Saclay. His field of expertise is signal and image processing, statistical machine learning and scientific computing applied primarily to functional brain imaging data (EEG, MEG, fMRI). His work is strongly interdisciplinary at the intersection with physics, computer science, software engineering and neuroscience. He has coauthored more than 30 journal papers and 50 conference papers since 2009. He is a core developer of the Scikit-Learn machine learning software (http://scikit-learn.org) which is heavily used both in industry and in academic research. He is at the origin and the leader of the development of the MNE-Python software (http://mne.tools) now used and developed across many labs worldwide. In 2015, he was awarded a Starting Grant by the European Research Council (ERC).


Julie Josse

Inria, France

Causal inference for observational clinical data

Fields of expertise

After beeing a Professor of Statistics and Machine Learning at Ecole Polytechnique and a visiting researcher at Google Brain Paris, Julie Josse has just joined Inria as an advanced researcher to set up a team in data-sciences for health. She is still teaching at Ecole Polytechnique in the Master of Data Science. Julie Josse's is an expert in handling missing values (inference, multiple imputation, matrix completion, missing non at random data, supervised learning with missing values). She gives many courses and tutorials on the subject and has created a platform to collect works and give resources to users. https://rmisstastic.netlify.app/, has organized ICML workshop, an R package missMDA etc. She has published over 50 articles and written 2 books in applied statistics. Her vocation is to push methodological innovation to bring useful application of her research to the user in particular in bio-sciences and health. Her curent research focuses on causal inferences techniques for personalized medicine. She lead an important project with the Traumabase group dedicated to the management of polytraumatized patients to help emergency doctors making decisions. Julie Josse is dedicated to reproducible research with the R statistical software: she has developed packages including FactoMineR to transfer her work, she is a member of the R foundation and of Rforwards to increase the participation of minorities in the community.


Marco Lorenzi

3IA Côte d'Azur, Nice, France
Researcher, Université Côte d’Azur, Inria Sophia Antipolis, France

in a session on the multimodal analysis of biomedical data

Fields of expertise

I am a tenured research scientist (CR) at Université Côte d’Azur, Inria Sophia Antipolis. My research interest is in the development of statistical and machine learning methods for the analysis of large-scale and heterogeneous biomedical data. Current research topics include Bayesian modeling and uncertainty quantification, time-series analysis, latent variable models, and federated learning.


Serena Villata

3IA Côte d'Azur, Nice, France
Researcher, Université Côte d’Azur, CNRS, Inria, I3S

in a session on the multimodal analysis of biomedical data

Fields of expertise

I am a tenured research scientist (CR) at CNRS in the I3S Laboratory. My research focuses on information extraction from text to support decision making. More precisely, current research topics include argument mining from different kinds of text, including clinical trials for evidence-based reasoning. We developed the ACTA tool to detect argumentative structures and PICO éléments from PubMed abstracts.


Chrysoula Zerva

Researcher at NaCTeM, University of Manchester, UK

in a session linked with the plenary lecture by Sophia Ananiadou demonstrating the National Centre for Text Mining (NaCTeM, UK) tools

Fields of expertise

Textual uncertainty identification and citation analysis for news and scholarly articles

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Organized by the French Health Data Hub, the unique gateway to access health data for public interest research in France, and some of France’s leading AI research institutions: MIAI Grenoble, 3IA Cote d’Azur (Nice) and PRAIRIE (Paris),

Supported by the French Association for Medical Informatics, the Paris Brain Institute – ICM and the University of Paris