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)

Stéphanie Allassonnière

University of Paris & PR[AI]RIE

Presentation of PR[AI]RIE (3IA)

Fields of expertise

Stéphanie Allassonnière is Professor of Applied Mathematics in University of Paris, School of medicine. She received her PhD degree in Applied Mathematics (2007), studied one year as postdoctoral fellow in the Center for Imaging Science, JHU, Baltimore. She joined the Applied Mathematics department of Ecole Polytechnique in 2008 as assistant professor and moved to University of Paris school of medicine in 2016 as Professor. Her researches focus on statistical analysis of medical databases and stochastic optimisation algorithms in order to understanding the common features of populations, designing classification, early prediction and decision support systems.


Luigi Antelmi

MSc. Biomedical engineering - PhD candidate at Université Côte d’Azur, INRIA Sophia Antipolis.

in a session on Handling heterogeneity in the analysis of biomedical information: statistical learning with multiple data types and federated datasets

Fields of expertise

statistical learning, multimodal brain image analysis, computational clinical neuroscience, variational inference


Irene Balelli

Inria Sophia Antipolis - Mééditeranée, Université Côte d’Azur, France

in a session on Handling heterogeneity in the analysis of biomedical information: statistical learning with multiple data types and federated datasets

Fields of expertise

I am a Postdoctoral researcher in the Epione team at INRIA Sohia Antipolis Méditerranée. My research is embedded in the frame of the ANR project Fed-BIOMED (Federated statistical learning for new generation meta-analyses of large-scale biomedical data). My current research concerns the development of statistical and privacy-compliant methods to handle decentralized heterogeneous biomedical data, with a focus on applications to neurodegenerative diseases. Previously, I worked in mathematical modeling of the immune response to vaccinations as a member of the SISTM team (INSERM U1219 Bordeaux Population Health), where I was involved in the European project EBOVAC. I received my Ph.D. from University Paris 13 in 2016, where I developed a probabilistic mathematical framework to model antibody affinity maturation of B-cells. My Ph.D. project was supported by the Labex (Laboratoire d'Excellence) Inflamex.


Simona Bottani

Aramis Lab, ICM (Paris Brain Institute), INRIA, Sorbonne Université

in a session on deep learning for medical imaging

Fields of expertise

3rd year PhD student at Aramis Lab. She works on the development of CAD systems for large clinical datasets. Neuroimaging, machine learning and deep learning


Ninon Burgos

PR[AI]RIE, 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;


Mauricio Diaz

Inria

in a session on Deep learning for medical imaging

Fields of expertise

Ph.D in Computer Science, Computer Vision & Image Processing Engineer


Stanley Durrleman

Directeur de recherche, Inria
Responsable de l'équipe ARAMIS, Institut du Cerveau (ICM)

in a session on Disease course mapping with longitudinal data

Fields of expertise

Disease modelling, neurodegenerative diseases, statistical learning, neuroimaging


Johann Faouzi

Paris Brain Institute, Inria, CNRS, Sorbonne University, Inserm

in a session on Deep learning for medical imaging

Fields of expertise

Machine learning, Python programming, time series analysis, Parkinson’s disease, impulse control disorders


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 being 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 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 current research focuses on causal inferences techniques for personalized medicine. She led 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.


Slim Karkar

Université Grenoble-Alpes

in a session on Learning algorithms for prediction of cancer outcomes from multiomic data

Fields of expertise

Dr. Slim Karkar graduated both in Physics and Biostatistics in Paris University, then defended his Ph.D. in neuroimaging at University of Strasbourg. His research involves development of statistical tools for classification on large scale data, such as protein similarity networks in evolutionary genomics, mixtures of forensic DNA samples or integration of heterogeneous data in Imaging-Genetics cohorts. He recently joined the BCM team in Grenoble to develop statistical frameworks for -omics data to quantify tumor heterogeneity.


Igor Koval

Paris Brain Institute & Inria

in a session on disease course mapping with longitudinal data

Fields of expertise

Postdoctoral Researcher at the Paris Brain Institute & Inria. The research involve to explore new ways to understand the brain evolution, in particular the dynamics of neurodegenerative diseases, thanks to Machine Learning tools. The goal of this research is to help designing clinical trials suited for neurodegenerative diseases. Also Deep Learning lead instructor at Le Wagon.

Fields of expertise: Applied Maths, Machine & Statistical Learning, Modelling of Neurodegenerative diseases progression


Thomas Lartigue

German Center for Neurodegenerative Diseases (DZNE)

in a session on Gaussian Graphical Model exploration and selection in high dimension low sample size setting

Fields of expertise

Thomas Lartigue, PhD, is a Research Fellow at the German Center for Neurodegenerative Diseases (DZNE). His PhD degree in Applied Mathematics (2020) was funded by INRIA and conducted at École polytechinque. His research interests include Computational Statistics and Machine Learning for biological data analysis, with a focus on hierarchical models, graphical models and high-dimension statistics.


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.


Etienne Maheux

R&D Engineer, ARAMIS team, Inria / Paris Brain Institute (ICM)

in a session on Disease course mapping with longitudinal data

Fields of expertise

Disease modelling, statistical learning, longitudinal data, neurodegenerative diseases


Imke Mayer

École des Hautes Études en Sciences Sociales (EHESS)
(Centre d’Analyse et de Mathématique Sociales (CAMS))

in a session on Causal inference for observational clinical data

Fields of expertise

Imke Mayer, MSc, is a PhD candidate in applied mathematics at the École des Hautes Études en Sciences Sociales (EHESS). She conducts her research under the supervision of Julie Josse and Jean-Pierre Nadal as part of the interdisciplinary TrauMatrix project with collaborators from the Parisian hospital trust AP-HP and from Capgemini Invent. Her research focuses on causal inference and machine learning for personalized medicine and decision support based on clinical data analysis, currently in the context of critical care management. Her PhD project has recently been awarded with a Google PhD fellowship in machine learning.


Marco Milanesio

Université Côte d'Azur, MSI, Inria Epione

in a session on Argumentative Analysis of Clinical Trials for Evidence-based Medicine

Fields of expertise

Marco Milanesio earned his PhD in Computer Science at the University of Turin (Italy) in 2010, with a thesis on "Layering Multi-Purpose Applications over Structured and Dependable P2P Systems". During his PhD, he worked on the security aspects of distributed systems, their architectures and features.

From 2011 to 2017, he was research engineer at Eurecom in the "Network and Security" (until 2016) and "Data Science" departments, where he worked on several topics: network performance measurements, QoE / QoS, distributed storage, virtualization, time series analysis and forecasting.

He joined the MSI in September 2017. His first research project is carried out within the Inria Epione team, where he works on distributed optimization and ETL (Extract/Transform/Load) pipelines for distributed analysis of medical images.

He has worked for the Medical Data Lab since its creation, by contributing to the design of the architecture and the deployment of the servers' infrastructure. Within the same lab, he currently manages the servers and works on the development of software for the processing of genetic data.


Juliette Ortholand

Research Engineer in ARAMIS team, ICM institute

in a session on Disease course mapping with longitudinal data

Fields of expertise

Research engineer graduated in data sciences from Sorbonne University (2020) and Mines Paristech (2019).


Pierre-Emmanuel Poulet

Inria Aramis Team, Paris Brain Institute

in a session on Disease course mapping with longitudinal data

Fields of expertise

PhD student in statistical models for neurodegenerative diseases


Magali Richard

CNRS, Research associate

in a session on Learning algorithms for prediction of cancer outcomes from multiomic data

Fields of expertise

Magali Richard is a CNRS research associate in computational genetics at the TIMC-IMAG laboratory, Grenoble, France. She is an expert in tumour heterogeneity quantification using different types of omic data. After a PhD in genetics and neurobiology, she studied the genetic architecture of regulatory networks and organims adaptability in dynamic environment. Few years ago, she was recruited as a CNRS researcher in Grenoble. Her project is to contribute to the understanding of the fundamental mechanisms of living systems by proposing an interdisciplinary approach based on the statistical analysis of biological data. She is particularly interested in the following questions: How to quantify intra and inter-tumor heterogeneity? How to estimate (epi)genetic deregulations at the single individual level?


Andrea Senacheribbe

Research Intern at Inria Sophia Antipolis, France

in a session on Handling heterogeneity in the analysis of biomedical information: statistical learning with multiple data types and federated datasets

Fields of expertise

I am a research intern at Inria Sophia Antipolis and a double degree master student at Télécom Paris (EURECOM) and Politecnico di Torino. My research interests are in machine learning, statistical learning and data science applied to biology and healthcare.


Arnaud Valladier

Research engineer at ARAMIS Lab, INRIA / Paris Brain Institute (ICM)

in a session on Disease course mapping with longitudinal data

Fields of expertise

Research engineer in Machine Learning and Disease modeling


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

Opening ceremony (Monday 9:45am CET)

Stéphanie Allassonnière

University of Paris & PR[AI]RIE

Presentation of PR[AI]RIE (3IA)

Fields of expertise

Stéphanie Allassonnière is Professor of Applied Mathematics in University of Paris, School of medicine. She received her PhD degree in Applied Mathematics (2007), studied one year as postdoctoral fellow in the Center for Imaging Science, JHU, Baltimore. She joined the Applied Mathematics department of Ecole Polytechnique in 2008 as assistant professor and moved to University of Paris school of medicine in 2016 as Professor. Her researches focus on statistical analysis of medical databases and stochastic optimisation algorithms in order to understanding the common features of populations, designing classification, early prediction and decision support systems.


Nicholas Ayache

Inria & 3IA Côte d'Azur

Presentation of 3IA Côte d'Azur

Fields of expertise

Nicholas Ayache is Research Director at the French Institute for Research in Digital Science and Technology (Inria), where he is head of the EPIONE research team dedicated to the development of the e-patient (digital patient) and e-medicine (digital medicine) models. Since 2019, he has been the Scientific Director of the Interdisciplinary Institute of Artificial Intelligence (3IA) Côte d'Azur.

Nicholas Ayache’s current research focuses on the design and development of artificial intelligence algorithms to guide the diagnosis, prognosis and therapy of patients based on their medical images and all available clinical, biological and behavioral data.

He has been a member of the French Academy of Sciences since 2014 and a member of the French Academy of Surgery since 2018. He was the first Scientific Director of the Institut Hospitalo-Universitaire (IHU) in Strasbourg (2012-2015) and a visiting professor at the Collège de France, holding the annual chair of Computer Science and Digital Sciences for the 2013-2014 academic year. Nicholas Ayache was a visiting researcher at MIT and Harvard in 2007. He is a member of several strategic councils in France and abroad. He published more than 400 highly cited scientific articles (h-index 107).

Nicholas Ayache is a Civil Engineer from the École Nationale Supérieure des Mines de Saint-Étienne (1980), holds a Master of Science from the University of California at Los Angeles (UCLA, 1981), a PhD and a Habilitation Thesis from the University of Paris Sud (1983 and 1988).


Emmanuel Bacry

CNRS (French National Centre for Scientific Research) and French Health Data Hub

Keynote as General Chair of AI4Health and Presentation of the Health Data Hub

Fields of expertise

Emmanuel Bacry graduated from ENS (École normale supérieure de Paris, obtained a PhD in Mathematics in 1992. In 1996, he obtained his Accreditation to Supervise Research (“Habilitation à diriger des recherches”). Emmanuel Bacry is currently Senior Researcher at CNRS (French National Centre for Scientific Research) at Paris Dauphine University and is Scientific Director of the French Health Data Hub. During more than 4 years, he was head of the Big Data & Data Science Initiative at Ecole Polytechnique He was one of the main organizers of the largest French Summer School on AI (“DS3 - Data Science Summer School”) on Ecole Polytechnique’s campus and is currently the general chair of the Summer/ Winter School AI4Health, organized by the Health Data Hub and three Interdisciplinary Institutes of Artificial Intelligence (3IA).
Emmanuel Bacry’s research has always been guided by a constant concern for applications, particularly in statistical finance, health and more recently in sustainable development..
Emmanuel Bacry has led for 6 years (2015-2020) a partnership between Ecole Polytechnique and the French National Health Insurance Fund (CNAM) to develop Big Data methodologies based on the national healthcare system claims databases (SNDS), one of the largest medico-administrative databases in the world. In this context, he works on various applications such as detecting weak signals in pharmacoepidemiology or optimization of health pathways of a given pathology.
As an AI expert, Emmanuel Bacry is a member of several scientific boards and committees including the AI board for the 2021/2 Universal Exposition which will take place in Dubai.
Since 2015, Emmanuel Bacry has led a partnership between Ecole Polytechnique and the French National Health Insurance Fund (CNAM) to develop Big Data methodologies based on the national healthcare system claims databases (SNDS), one of the largest medico-administrative databases in the world. In this context, he works on various applications such as detecting weak signals in pharmacoepidemiology or optimization of health pathways of a given pathology.
As an AI expert, Emmanuel Bacry is a member of several scientific boards and committees including the AI board for the 2021/2 Universal Exposition which will take place in Dubai.


Alexandre Moreau-Gaudry

Grenoble Alpes University Hospital, University of Grenoble

Presentation of 3IA MIAI Grenoble

Fields of expertise

A. Moreau-Gaudry graduated from ENSIMAG in 1995, obtained a PhD in
Applied Mathematics in 2000 and in 2012 his accreditation to direct
research ("Habilitation à diriger des recherches"). He also undertook
medical studies and obtained a doctorate in medicine in 2004. Since
2013, he has been a professor of public health (biostatistics, medical
informatics, communication technologies) at the University of Grenoble
Alpes and a hospital practitioner at the CHU Grenoble Alpes in the
Department of Public Health. He currently directs the Clinical
Investigation Center (CIC1406, INSERM / CHUGA / UGA) in Grenoble, the
Clinical Investigation Center - Innovative Technologies (CIC-IT) that he
co-founded in 2008, and the french national network of CIC-IT. He also
co-directs PREDIMED, the data lake of the Grenoble Alpes University
Hospital authorized by the CNIL on 10/2019. He conducts his research
within the TIMC laboratory (UMR 5525 CNRS / UGA) in the field of CAMI,
more particularly in the field of navigated orthopaedics and navigated
interventional radiology. His current research focuses on the
development of new technological innovations based on advances in AI in
order to improve patient care in the context of 4P medicine.

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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

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