Guests
Cédric VILLANI
Mathematician and LREM Deputy
Philippe CONTICINI
Distinguished Pastry Chef
Speakers
Dominique BOULLIER
Sociologist, Linguist – Digital Humanities Institute – EPFL (École Polytechnique Fédérale de Lausanne)
Social Sciences Models Encapsulated in Data Science Practices
Nicoló CESA-BIANCHI
Professor – University of Milan
Online Learning Algorithms
Mark GIROLAMI
Professor – Imperial College London,
Director of the Lloyds Register Foundation, Turing Programme on Data Centric Engineering
Probabilistic Numerical Methods
Hayit GREENSPAN
Professor – Tel Aviv University
Deep Learning for Medical Imaging
Krishna GUMMADI
Professor – Max Planck Institute for Software Systems
Fairness in Machine Learning
Mireille HILDEBRANDT
Research Professor of Interfacing Law and Technology – Vrije Universiteit Brussel
Machine Learning Research Design and the GDPR
Yann LECUN
Director of AI Research – Facebook,
Professor – Courant Institute, New York University
Deep Learning
Patrick LOISEAU
Holder of a Chair of Excellence, Univ. Grenoble Alpes / LIG
Privacy, fairness and transparency challenges in social media targeted advertising
Volker ROTH
Professor – University of Basel
Deep Latent Variable Models in Medical Informatics
Suvrit SRA
Assistant Professor – MIT
Non-convex Optimization
Brandon STEWART
Assistant Professor – Princeton University
How to Make Causal Inferences Using Texts
Jean-Philippe VERT
Director, Centre for Computational Biology (CBIO) at MINES ParisTech, Institut Curie and INSERM, and Research Professor, Department of Mathematics and Applications, ENS Paris
Machine Learning for Precision Medicine
Adrian WELLER
Senior Research Fellow – University of Cambridge,
Programme Director for AI – Alan Turing Institute
Trust and Transparency
In-depth Tutorial with Practical Session Speakers
Chloé-Agathe AZENCOTT
Researcher at the Centre for Computational Biology (CBIO) of Mines ParisTech, Institut Curie and INSERM
Machine Learning for Genetic Data and Biomedical Images
Nicolas COURTY
Associate Professor – University of Bretagne Sud
Optimal Transport and Machine Learning
Marco CUTURI
Professor – ENSAE
Optimal Transport and Machine Learning
Pawel DLOTKO
Assistant Professor – Swansea University
Topological Data Analysis
Rémi FLAMARY
Assistant Professor – University of Nice
Optimal Transport and Machine Learning
Arthur GRETTON
Professor – University College London
Representing and Comparing Probabilities with Kernels
Olivier GRISEL
Software Engineer – Inria
Introduction to Deep Learning with Keras
Julie JOSSE
Professor – École Polytechnique
Missing Data Imputation
Emtiyaz KHAN
Professor – RIKEN
Approximate Bayesian Inference: Old and New
Olivier KOCH
Staff Machine Learning Lead – Criteo
Recommendation in the Real World
Andreas KRAUSE
Professor – ETH Zürich,
Academic Co-Director of the Swiss Data Science Center
Submodularity in Data Science
Vitaliy KURLIN
Associate Professor – University of Liverpool, Data Scientist – Materials Innovation Factory
Topological Data Analysis
Francisco MASSA
Research Engineer – Facebook AI Research
Crash Course in Deep Learning and PyTorch
Olivier PIETQUIN
Research Scientist – Google Brain
Reinforcement Learning
Vincent ROUVREAU
Research Software Engineer – Inria
Topological Data Analysis
Brandon STEWART
Assistant Professor – Princeton University
Text as Data in Social Sciences
Flavian VASILE
Research Lead, Learning Representations team – Criteo
Recommendation in the Real World
Jean-Philippe VERT
Director, Centre for Computational Biology (CBIO) at MINES ParisTech, Institut Curie and INSERM, and Research Professor, Department of Mathematics and Applications, ENS Paris
Machine Learning for Genetic Data and Biomedical Images
Thomas WALTER
Researcher at the Centre for Computational Biology (CBIO) of Mines ParisTech, Institut Curie and INSERM
Machine Learning for Genetic Data and Biomedical Images
Kun ZHANG
Assistant Professor – Carnegie Mellon University
Causality and Machine Learning
Teaching Assistants
Mingming GONG
PostDoc – University of Pittsburgh & Carnegie Mellon University
Causality and Machine Learning
Aaron MISHKIN
Undergraduate Student – University of British Columbia
Approximate Bayesian Inference: Old and New
Cambria NASLUND
PhD student – Princeton University
Text as Data in Social Sciences
Peter NAYLOR
PhD student, Centre for Computational
Biology (CBIO) of Mines ParisTech, Institut Curie and INSERM
Machine Learning for Genetic Data and Biomedical Images
Didrik NIELSEN
Research Assistant, RIKEN
Approximate Bayesian Inference: Old and New
Mohammad Reza KARIMI
MSc student, ETH Zürich
Submodularity in Data Science
Pierre RICHEMOND
PhD Student, Imperial College London
Reinforcement Learning
Krasen SAMARDZHIEV
PhD student – University of Liverpool
Topological Data Analysis
Heiko STRATHMANN
PhD student – University College London
Representing and Comparing Probabilities with Kernels