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Inference from Evolving Populations: Agriculture

Presented by: 
Maud Lemercier University of Warwick, The Alan Turing Institute
Date: 
Tuesday 16th March 2021 - 11:00 to 11:25
Venue: 
INI Seminar Room 1
Session Title: 
Understanding Clouds of Paths and Their Applications
Session Chair: 
Thomas Cass
Abstract: 
Inferring properties about time-evolving populations is a widespread problem, yet a non-standard machine learning task. Most existing machine learning models can either handle a static snapshot of a population or a single trajectory. In this talk I will present a generic framework, based on the expected signature which enables to compactly summarize a cloud of time series and make decisions on it. I will discuss an application in agricultural monitoring, where a key challenge is to predict the yield before harvest using a collection of time series acquired by satellite-sensors.
University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons