Alessandro Blasimme is a reader in bioethics at Swiss Federal Institute of Technology (ETH Zürich).

He has held research appointments at the French National Institute of Health and Medical Research (INSERM) as well as the University of Zürich, before joining ETH Zürich. In 2013 he received a Fulbright-Schuman Scholarship to undertake research at the Program on Science, Technology and Society, Harvard University (USA) where he was a fellow. His research focuses on ethical and policy issues in biomedical innovation and biotechnology. His areas of expertise include translational medicine, precision medicine, regenerative medicine, genetic engineering, digital health and ageing. He has published widely in leading bioethics and medical journals and he is a principal investigator in national as well as international research consortia.

He is a visiting professor in bioethics at La Sapienza University of Rome.

Introductory keynote: “Artificial intelligence in medicine: an ethical perspective.“
(1 hour)

MD, Consultant Anesthesia and Critical Care, Emergency Operating Theatre and Resuscitation Room, Division Anesthesia-Critical Care, Grenoble Alpes University Hospital, Prehospital Emergency Medicine, SAMU 38, Traumabase Group, Réseau Urg’ARA

Born and trained in Germany, specialty training Anesthesia-Critical Care-Prehospital Emergency Medicine Germany and France. Lead Consultant Trauma Critical Care and Resuscitation Beaujon University Hospital, Paris until 2021, since 2021 Consultant Grenoble University Hospital; clinical lead regional trauma network Auvergne-Rhône-Alpes; cofounder French Trauma registry Traumabase.eu. Research focus machine learning for decision support (traumatrix.fr), shock management.

Use case: “Machine learning in the triage of major trauma, the Traumatrix project“
(1 hour)

Dr Anna Goldenberg is a professor in the departments of Computer Science and the Laboratory Medicine and Pathobiology at the University of Toronto.

Dr Anna Goldenberg is a Varma Family Chair in Biomedical Informatics and Artificial Intelligence at SickKids Research Institute as well as a CIFAR AI chair at the Vector Institute. Dr Goldenberg trained in machine learning at Carnegie Mellon University, with a postdoctoral focus in computational biology and medicine. The current focus of her lab is on developing and deploying machine learning models to healthcare. Dr Goldenberg’s lab is strongly committed to creating responsible AI to benefit patients across a variety of conditions.

Plenary course: “AI for healthcare time series.“
(3 hours)

Dr. Faisal Mahmood is an associate Professor of Pathology at Harvard Medical School

Dr. Mahmood is an Associate Professor of Pathology at Harvard Medical School and the Division of Computational Pathology at the Brigham and Women’s Hospital. He received his Ph.D. in Biomedical Imaging from the Okinawa Institute of Science and Technology, Japan and was a postdoctoral fellow at the department of biomedical engineering at Johns Hopkins University. His research interests include pathology image analysis, morphological feature, and biomarker discovery using data fusion and multimodal analysis. Dr. Mahmood is a full member of the Dana-Farber Cancer Institute / Harvard Cancer Center ; an Associate Member of the Broad Institute of Harvard and MIT, and a member of the Harvard Bioinformatics and Integrative Genomics (BIG) faculty.

Use case: “Deep learning for pathology image analysis“
(2 hours)

Bjoen Menze is a computer scientist working in the field of biomedical image analysis and a professor in the Department for Quantitative Biomedicine at the University of Zurich.

Bjoen Menze was a professor in the Department of Informatics at TU München (W3 level), a Rudolf Moessbauer Tenure Track Professor of the TUM Institute for Advanced Study (W2 level), and a guest professor at Maastricht University. As postdoc, Bjoen Menze was a member of the Asclepios team of the Inria Sophia-Antipolis, the Computer Vision Lab at ETHZ, and the CSAIL Medical Vision Group at MIT, as well as the Department of Anthropology of Harvard FAS, and the Surgical Planning Lab at Brigham Women’s Hospital. Bjoen Menze received a PhD from Heidelberg University in 2007. He organized workshops at MICCAI, ISBI, NeurIPS, and CVPR in the fields of medical computer vision and neuroimage processing, served as guest editor for Medical Image Analysis and as a member of the program committee of MICCAI, and he is a member of the editorial board of Medical Image Analysis. Bjoen Menze received the Medical Image Analysis Award for the best paper of MICCAI 2014, and the Young Scientist Publication Impact Award at MICCAI 2015. Bjoen Menze served as a general chair for MIDL 2022.

Plenary course: “Deep learning for medical image data –  from images to structures to graphs“
(3 hours)

Professor Sebastien Ourselin is Head of the School of Biomedical Engineering & Imaging Sciences, King’s College London and Deputy Director of the London Medical Imaging and Artificial Intelligence Centre for Value Based Healthcare.

Prior to this, Professor Sebastien Ourselin was at University College London where he served as Vice-Dean for Health (Faculty of Engineering) and Director of the Institute of Healthcare Engineering.He has over 20 years of experience within academia and research organisations across three countries. Alongside Guy’s & St Thomas’ NHS Foundation Trust, he is leading the establishment of a MedTech Hub, located at St Thomas’ campus. The vision for the Hub is to create a unique infrastructure, enabling academia, industry and the NHS to work in synergy and develop health technologies (including medical devices), workforce and operational improvements that will be of global significance. He has significant experience in translating and commercialising healthcare technology and is a co-founding member of two academic spin-out companies, BrainMiner Ltd (which utilises machine learning algorithms for brain image analysis) and Hypervision Surgical Ltd (which aims to deliver artificial intelligence -enabled hyperspectral imaging in the operating room). Over the last 20 years, he has raised over £60M as Principal Investigator and has published over 550 articles (over 42451 citations, h-index 101). He is an elected Fellow of the Royal Academy of Engineering (FREng, 2021), Fellow of the Institute of Physics and Engineering in Medicine (FIPEM, 2021) and Fellow of the Medical Image Computing and Computer Assisted Intervention Society in (FMICCAI, 2016). Pr Ourselin also holds an international chair at the Interdisciplinary Institutes for Artificial Intelligence 3IA Côte d’Azur.

Use case: Learning across 16M patients: platforms and applications in acute care
(1 hour)

Petra Ritter is Head of the Brain Simulation Section at the Charité University Medicine Berlin and Berlin Institute of Health

Petra Ritter heads the Brain Simulation Section at the Charité University Medicine Berlin and Berlin Institute of Health. Her research focus is on integrating neuroimaging and computational neuroscience to discover mechanisms of brain function and dysfunction. She serves in the leadership of large EU infrastructure projects such as Testing and Experimentation Facility Health AI and Robotics (TEF-Health), Virtual Brain Cloud & eBRAIN-Health and is directing EBRAINS Health Data Cloud. Petra Ritter studied medicine in Berlin, Germany and in the US. At the Charité she got appointed a Johanna Quandt lifetime Professorship for Brain Simulation in 2017. Prof. Ritter serves as the Director for International Affairs at the Charité.

Title: “How complex simulations augment artificial intelligence“
(1 hour)

Use case: Implicity : how an AI concept becomes an approved medical device used daily by healthcare professionals
(1 hour)

Dr. Andrzej Ryś is the Principal Scientific Adviser, Directorate-General for Health and Food Safety (DG SANTE), European Commission.
He is also a medical doctor specialised in radiology and public health, graduated from Jagiellonian University, Krakow, Poland.

In 1991, he founded the School of Public Health at the Jagiellonian University that he ran as Director until 1997. Thereafter, from 1997 to 1999, he served as Director of the Krakow’s City Health Department. Between 1999 and 2002, he continued his career as Deputy Minister of Health in Poland where he developed a new system of emergency medical service and reformed the education system for the health professionals. At the same time, he was a member of the Polish EU accession negotiators team for the harmonisation of the Polish Health Care Law with the EU’s Acquis Communautaire.

After becoming Senior Consultant of “Health and Management Ltd” for the World Bank, WHO and EAR in Serbia in 2002. In 2003 he founded the “Center for Innovation, Technology Transfer and University Development” (CITTRU) at the Jagiellonian University where he assumed the role of Director until 2006. From 2004 to 2005, he was also Director of Development at “Diagnostyka Ltd”. In 2006, he became Director for Public Health and Risk Assessment at the Directorate-General for Health and Consumers (DG SANCO), in the European Commission. From 2011 to September 2022, Dr. Andrzej Rys was the Director responsible for Health Systems, Medical Products and Innovation in DG SANTE, in the European Commission in Brussels, Belgium, where he is now Principal Scientific Adviser since 1 October 2022.

Introductory keynote: “Digital health: policy and regulatory landscape in the European Union and globally“
(1 hour)

Serena Villata is a research director in computer science at the CNRS and she pursues her research at the I3S laboratory in Sophia Antipolis (France).

Her research area is Artificial Intelligence (AI), and her current work focuses on Natural Language Processing, with a specific focus on medical texts, political debates and social network harmful content (abusive language, disinformation). She is the co-leader of the EU project ANTIDOTE about generating natural language explanations for medicine. She is the author of more than 150 scientific publications in AI. Since July 2019, she has been awarded with a Chaire in Artificial Intelligence at the Interdisciplinary Institute for Artificial Intelligence 3IA Cote d’Azur on “Artificial Argumentation for Humans”. She became the Deputy Scientific Director of the 3IA Côte d’Azur Institute in January 2021. Since December 2019, she is a member of the National Committee for Digital Ethics (CNPEN).

Plenary course: “Clinical Text Analysis: Methods and Applications.“
(3 hours)

Alex Adam is a master’s degree graduate in computer science from the University of Toronto in 2019, Alex is currently pursuing a PhD.

Alex graduated with a Master’s degree in computer science from the University of Toronto in 2019, and is currently pursuing a PhD. Alex is interested in building machine learning models
that have stable performance once deployed in the real world. The unanticipated influence of model predictions on the outcomes of future samples, as well as the complex trust dynamics which occur between human users and ML models are his primary focus. Alex believes that to safely harness the power of AI, one must be able to detect when predictions are no longer reliable, and determine how to optimally update models on newly gathered data.

Pratical Session: “Machine learning for time series with applications to healthcare“

Tamaz Amiranashvili is a PhD candidate in medical image and shape analysis under Prof. Dr. Bjoern Menze and Dr. Stefan Zachow at Technical University of Munich and University of Zurich.

After majoring in computer science at the Freie Universitaet Berlin, Tamaz Amiranashvili has worked on developing solutions for patient-specific therapy planning at 1000shapes GmbH in
Berlin. In research, his main interest lies in creation of data-driven, deep-learning based statistical shape models with application to ill-posed problems in the medical imaging domain, such as shape reconstruction and image segmentation.

Pratical Session: “Deep learning for medical image processing“

Francesco Cremonesi is a Research engineer.

Francesco is a Research and Development Engineer motivated by bridging the gap between the collection of high quality datasets and their analysis through well-engineered code and robust learning models. After a PhD at the boundary between computational neuroscience and high performance computing, Francesco has gained professional experience as a data engineer as well as a project manager for EU Horizon projects with partners from the clinical, academic and engineering domains. He is now a core developer of the federated learning software Fed-BioMed, and spends his time writing Python code for new features or supporting researchers through manuscript redaction and training of machine learning models. When he’s not coding, Francesco likes to play basketball, learn guitar, and develop his passion for photograph.

Pratical Session: “Fed-BioMed, an open source framework for federated learning in real world healthcare applications“

Stanley Durrleman is a Senior research scientist at Inria, Team leader at the Paris Brain Institute (ICM), Fellow of the Paris AI Research Institute (PRAIRIE).

Dr. Stanley Durrleman is a professor of mathematics, senior researcher at Inria, and team leader at the Paris Brain Institute (ICM) at the Pitié-Salpêtrière hospital. He is a fellow of the Paris AI research institute – PRAIRIE.
His research interests lie in modeling and predicting the progression of neurodegenerative diseases. He has invented and patented new methods to build digital models of disease progression. These “digital twins” simulate how the brain structure and function of a patient will change in the future. They serve to design precision clinical trials and evaluate therapeutic interventions.
S. Durrleman has received international recognition for his research including the second Gilles Kahn Award for the best dissertation in computer science in 2010, a starting grant from the European research council (ERC) in 2015. He was the first laureate of a Sanofi iDEA award outside the USA in 2019. In 2020, he received the Inria – Académie des Sciences young researcher award.
In 2022, he founded the company Qairnel to build the first virtual clinic dedicated to neurocognitive disorders and ease the access to diagnostic and therapeutic innovations for anyone in need.

Pratical Session: “Disease course mapping with longitudinal data“

Lucia Innocenti is a PhD Student at INRIA, under the supervision of Sebastien Ourselin and Marco Lorenzi.

Lucia is a Ph.D. student at INRIA, supervised by the 3IA Côte d’Azur Chairs Prof. Sebastien Ourselin and Dr. Marco Lorenzi. Graduate in Data Science and Engineering from the Polytechnic of Turin with a thesis on Audio-Visual Human Activity Recognition for Humanoid Robotics, she is now working on Artificial Intelligence for Medicine. Her thesis topic is “Multi-centric AI-based frame-work for prostate cancer patients management on active surveillance through robust federated learning.” Currently, she is studying and analyzing different distributed training models, with a focus on efficiency and cost-effectiveness.

Pratical Session: “Fed-BioMed, an open source framework for federated learning in real world healthcare applications“

Guillaume Jaume is a Postdoctoral research fellow at Harvard Medical School in Mahmood Lab.

Guillaume Jaume is a postdoctoral research fellow at Harvard Medical School in Mahmood Lab. He holds a PhD in electrical engineering and computer science from the Ecole Polytechnique Federale de Lausanne (EPFL), obtained in collaboration with IBM Research – Europe and ETH – Zurich under the supervision of Jean-Philippe Thiran and Maria Gabrani.

Pratical Session: “Fundamentals of Deep Learning for Pathology Image Analysis“

Sofia Kaisaridi is a PhD student working at the Paris Brain Institute in the Aramis team, under the supervision of Sophie Tezenas du Montcel.

Sofia Kaisaridi’s work is focused on the modelisation of the global cognitive decline in neurodegenerative diseases. With the use of longitudinal data from patients with CADASIL (cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy), she is trying to identify the patterns of the disease trajectory and study how they are impacted by different covariates. Sofia originally studied mathematics, in the National Kapodistrian University of Athens, Greece and continued her studies with a M2 Statistique, modélisation et science des données en santé at Sorbonne Université, Paris. Before starting her PhD she also worked for one year as a biostatistician at the Institut Pierre Louis d‘Épidémiologie et de Santé Publique in Paris on the COCOPREV project : “Prevention of complications of COVID-19 in high-risk patients infected with SARS-CoV-2 eligible for treatments under an ATU. A prospective cohort”.

Pratical Session: “Disease course mapping with longitudinal data“

Hongwei Li is a postdoctoral researcher at Technical University of Munich and University of Zurich.

Hongwei (Bran) works on medical image analysis and machine learning with Bjoern Menze at the University of Zurich, Benedikt Wiestler, and Daniel Rueckert at TUM. His research interests include image segmentation, image synthesis, representation learning, and generative models. He finished his Ph.D. program in medical image analysis at TUM between 09.2017 and 11.2022, spending wonderful five years in Munich and Zurich. He was one of the main organizers of multiple MICCAI image analysis challenges, such as fetal tissue segmentation (FeTA 2021-2022) and uncertainty quantification of image segmentation (QUBIQ 2020-2021). Personal website: https://hongweilibran.github.io/.

Pratical Session: “Deep learning for medical image processing“

Santiago Marro is a PhD student at Université Côte d’Azur, under the supervision of Serena Villata and Elena Cabrio.

Santiago Marro is a PhD student at Université Côte d’Azur, specializing in Natural Language Processing (NLP). He has been pursuing his PhD in France for three years, with a particular focus on applying his research to the medical field. Santiago’s research focuses on argumentation mining, argument quality assessment and generation, and explainability AI. Santiago has extensive experience working with clinical texts and other types of medical texts. His research seeks to explore the intersection between healthcare and AI, with a focus on using NLP techniques to improve medical decision-making. Santiago has multiple publications in international AI conferences that demonstrate his expertise and contributions to the field.
In addition to his work in NLP and healthcare, Santiago has a keen interest in explainability AI. His research aims to make AI more transparent and understandable to both healthcare professionals and patients.

Pratical Session: “Information Extraction and Argument Mining for Medicine“

Data scientist

Benjamin Navet is a data scientist with a huge interest for natural language processing. He graduated with a master degree in computer science from Rennes university. After graduation, he joined the team of 3IA techpool from 3IA Côte d’Azur located at Sophia Antipolis and have been working on a number of projects related to natural language processing. He is passionate about using technology to solve real-world problems and dedicated to staying at the forefront of the industry.

Pratical Session: “Information Extraction and Argument Mining for Medicine“

Juliette ORTHOLAND studies Amyotrophic Lateral Sclerosis (ALS) from a modelling perspective. She extends current longitudinal models to joint survival and longitudinal models in order to improve their reconstruction and prediction performances, as well as to model a critical outcome in ALS: death.

Pratical Session: “Disease course mapping with longitudinal data”

Pierre Emmanuel is a PhD student in the Inria ARAMIS team at the Paris Brain Institute, under the supervision of Stanley Durrleman

Pierre Emmanuel is a PhD student in statistical models for neurodegenerative diseases.

Pratical Session: “Disease course mapping with longitudinal data“

Sana Tonekaboni is a Doctoral candidate in her final year in the Machine Learning Group at the University of Toronto and the Vector Institute, under the supervision of Anna Goldenberg.

Sana Tonekaboni focuses on applying machine learning to healthcare and developing strategies to overcome obstacles to the integration of AI in clinical contexts. Her research encompasses a wide range of topics, including explainable AI, unsupervised representation learning, and Bayesian modeling for medical time series. Sana has been recognized as an Apple Scholar in AI/ML and was a former health system impact fellow of the Canadian Institute of Health Research (CIHR). 

Pratical Session: “Machine learning for time series with applications to healthcare“