› Using supervised machine learning methods to infer demographic history from genomic data - Arnaud Quelin, Éco-Anthropologie, Laboratoire Interdisciplinaire des Sciences du Numérique
10:00-10:30 (30min)
› Unifying Ideas from the Sequentially Markovian Coalescence with Deep Learning for Population Genomic Inference - Kevin Korfmann, Technische Universität München = Technical University of Munich
10:30-11:00 (30min)
› Scalable CNN-based classification of selective sweeps using derived allele frequencies - Sjoerd Van den Belt, University of Twente
11:30-12:00 (30min)
› Detecting selective sweeps using FPGA-accelerated spiking convolutional neural networks - Hanqing Zhao, University of Twente
11:30-12:00 (30min)
› Detecting Positive Selection using Convolutional Neural Networks - Charlotte West, European Bioinformatics Institute [Hinxton]
12:30-13:00 (30min)
› Improving tree representation and neural network architecture for deep learning from phylogenies in phylodynamics and diversification studies - Manolo Perez, Muséum national d'Histoire naturelle, Imperial College London
14:30-15:00 (30min)
› Transformers for EpiDemiological DYnamics: from genomic data to epidemiological parameters - Vincent GAROT, Biologie Computationnelle et Quantitative = Laboratory of Computational and Quantitative Biology, Centre interdisciplinaire de recherche en biologie
15:00-15:30 (30min)
› Assessing the power of artificial intelligence approaches for birth-death model classification - Pablo Gutiérrez de la Peña, Real Jardín Botánico - CSIC
15:30-16:00 (30min)