This year, the challenges were organized and supervised at the ENS by Rudy Morel, Tanguy Marchand, Florenting Guth, Louis Thiry, Gaspard Rochette, and John Zarka. The organization of these data challenges is supported by the CFM Chair at the École normale supérieure, and by the Fondation des Sciences Mathématiques de Paris.
Data challenges
Challenges 2020
Where will the next stock transaction take place? ?
Presented by Eric Lebigot of Capital Fund Management
Given the order book and recent transactions, the aim of this challenge is to predict on which platform the next trade of a given stock will occur.
Predicting mortality risk
Presented by Rémy Dubois from Owkin
The aim of this challenge is to predict a patient's survival time from CT scans in the form of 3D images and clinical data in the form of previously extracted descriptors.
NLP applied to the analysis of legal decisions
Presented by Matthieu Mazzolini from Kayrros
The aim of this challenge is to estimate the production of industrial group sites based on daily measurements and capacity data.
Estimated industrial production
Presented by Matthieu Mazzolini from Kayrros
The aim of this challenge is to estimate the production of industrial group sites based on daily measurements and capacity data.
Multimodal classification of product data
Presented by Laurent Ach from Rakuten
The aim of this challenge is to perform a multimodal classification of texts and articles into product classes.
Stock yield prediction
Presented by Karl Bartoli from QRT
The aim of this challenge is to predict the returns of a stock on the US market based on the returns of the last twenty days.
AI applied to meter reading
Presented by Nicolas Daviaud from Suez
The aim of this challenge is to create an algorithm that reads the value of a meter from images taken under various angles and conditions.
Metamodels to improve energy consumption and comfort in large buildings
Presented by Sylvain Le Corff from Oze-Energies
The aim of this challenge is to predict the energy consumption and temperature of a building based on sensor readings.
Song classification of ten species of Odontocetes
Presented by Hervé Glotin, Université de Toulon
The aim of this challenge is to classify the sound recordings of marine animals into ten categories.
Gender prediction based on brain rhythm
Presented by Valentin Thorey from Dreem
The aim of this challenge is to predict the gender of individuals based on forty intervals of their electrocardiogram.
Next-day wind power forecast on the energy market
Presented by Olivier Vannier from CNR
The aim of this challenge is to predict the energy production of six wind power plants based on meteorological data.
Predicting the response time of a Paris Fire Brigade vehicle
Presented by Eric Manouvrier from Valeo
The aim of this challenge is to predict defects in factory parts during assembly. This prediction can be based on industrial data, angle values, logical measurements and other data.
Predicting Bitcoin's direction from sentiment data
Presented by Olivier Croissant of Natixis
The aim of this challenge is to build a model producing an optimal option replication strategy based on the asset value over time and other market values such as volatility.
Failure prediction in a production line
Presented by Eric Manouvrier from Valeo
The aim of this challenge is to predict defects in factory parts during assembly. This prediction can be based on industrial data, angle values, logical measurements and other data.
Object segmentation from computer-generated images
Presented by Olivier Croissant of Natixis
The aim of this challenge is to build a model producing an optimal option replication strategy based on the asset value over time and other market values such as volatility.
Disaggregating the load curve of a dwelling
Presented by Olivier Croissant from Natixis
The aim of this challenge is to build a model producing an optimal option replication strategy based on the asset value over time and other market values such as volatility.
Equinox coverage using machine learning
Presented by Olivier Croissant from Natixis
The aim of this challenge is to build a model producing an optimal option replication strategy based on the asset value over time and other market values such as volatility.
