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 GOND

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