This year, the challenges were organized and supervised at ENS by Tomas Anglès, Louis Thiry, Roberto Leonarduzzi 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 2019
Predicting daily movements in US equities
Presented by Éric Lebigot of Capital Fund Management
The objective of the challenge is to predict the sign of US stock returns over a 30 minute period at the end of the day, based on their historical returns over 5 minute windows at the beginning of the day.
Detecting breast cancer metastases
Presented byCharlie Saillard from Owkin
The aim of the challenge is to determine the presence of lymph node metastases in histological section images of breast cancer patients. This challenge is " weakly supervised ", the presence or absence of metastases being indicated locally for some images, but only globally at the scale of the entire image for most sections.
Prediction of dynamic electricity profiles
Presented by Pierre Cauchois from Enedis
The aim of the challenge is to estimate 7 series of dynamic electricity profiles representing the consumption patterns of different customer categories in the mass market, based on energy and weather data.
Sharpe ratio prediction for mixtures of quantitative strategies
Presented by Stefan Duprey from Napoléon Crypto
The objective of the challenge is to predict the Sharpe ratio, a risk-adjusted return indicator, of determined combinations of 7 quantitative strategies over a period of 5 business days, based on the 21 business day history of returns of these 7 strategies as well as 3 relevant financial indicators.
Predicting slow-wave brain activity during deep sleep
Presented by Valentin Thorey from Dreem
The aim of the challenge is to predict, from 10 seconds of EEG signals of a slow brain wave during deep sleep, as well as various indicators of sleep quality up to that point, whether another slow wave of low or high amplitude will follow.
Prediction of expected response to pharmaceutical questions
Presented by Emmanuel Bilbault from Posos
The aim of the challenge is to categorize pharmaceutical questions according to the type of answer expected.
Solving the Rubik's cube 2x2x2
Presented by Julien Peyras from LumenAI
The aim of the challenge is to predict, from a certain initial configuration of a 2x2x2 Rubik's cube, the minimum number of moves required to reach the solution.
Prediction of the spatiotemporal concentration of fine particles PM10
Presented by Grégoire Jauvion of Plume Labs
The aim of the challenge is to predict PM10 levels measured by certain air quality monitoring stations, based on measurements provided by adjacent monitoring stations and certain urban characteristics.
Prediction of rat brain activity from temporal patterns of action potentials
Presented by Ilya Prokin of the ENS Group for Neural Theory (GNT)
The aim of the challenge is to predict the state of a rat's brain activity from the times of occurrence of action potentials within a certain hippocampal neuron.
Electricity consumption prediction for pricing the electricity supplied
Presented by Alexis Lucido of BCM Energy.
The aim of the challenge is to predict the electricity consumption of two potential customers over the course of a year, based on a set of consumption histories of other customers with similar characteristics but geographically distant, as well as geographical and meteorological data.
Screening and diagnosis of esophageal cancer using in vivo images
Presented by Fanny Louvet-de Verchère of Mauna Kea Technologies
The aim of the challenge is to classify endoscopic images of the esophagus into 4 classes representing different stages of esophageal cancer.
Building loss prediction
Presented by Clémence Devries from Generali
The aim of the challenge is to predict the water damage claims experience of an insured building over a period of one year, based on its characteristics.
Classifying and optimizing quality of life at work
Presented by Sylvain Le Corff of Oze-Energies
The aim of the challenge is to predict the comfort felt by building occupants, based on environmental data measured in real time by sensors within the building.
Pricing exotic options using multidimensional nonlinear interpolation
Presented by Olivier Croissant from Natixis
The aim of the challenge is to predict the value of exotic options contained in debt securities, based on 23 parameters characterizing them.
