Evolutionary genomics and population genetics investigate patterns of genetic diversity between species or between populations within a species and play a fundamental role in many aspects, from theoretical facets of evolution to practical ones, such as conservation genetics and biomedical sciences.

Methodologies have always been a strong interest of the community, from the development of mathematical models to the design of statistical inference tools, leading to numerous biological discoveries. These developments helped to adapt very quickly to the continuous influx of data, which has not only dramatically increased in quantity but also keeps changing in terms of quality and type.

Among the methodological frameworks, machine learning has emerged as a promising way of analysing large and complex datasets. The application of AI, and particularly deep learning, to evolutionary genomics is still in its infancy while showing promising initial results. It is currently applied to a variety of tasks, such as the inference of demographic history, ancestry, natural selection, phylogeny, species delimitation and diversification.

However, machine learning methods in evolutionary genomics and population genetics face unique challenges, including identifying appropriate assumptions about the evolutionary process and how to simulate it, and identifying the best ways to handle sequences, sequence alignments, phylogenetic trees, or additional information, such as geographical maps, temporal labels and environmental covariates. Overcoming these challenges requires a collaborative effort. The goal of this conference is to foster this effort by allowing interested researchers to meet and share their work.

	Sara Mathieson is an Assistant Professor of Computer Science at Haverford College. Her lab develops machine learning algorithms for evolutionary inference, with a focus on generative approaches that adapt to diverse populations. She also works on machine learning interpretability, data privacy, and pedigree-based methods for characterizing recent evolution. Title: Generative adversarial networks, transfer learning, and interpretability for evolutionary inference
	Hélène Morlon did her training in mathematics, followed by her Masters and PhD in ecology in France. She then spent 5 years ½ as a postdoctoral researcher in ecology and evolution at UC Merced, the UO Eugene, UPenn and UC Berkeley. She is now a CNRS research director at the Institute of Biology of the Ecole Normale Supérieure. Her lab is interested in understanding how historical and contemporary processes shape present-day patterns of biological diversity.    Title: Deep learning for the phylogenetic inference of species diversification
	Pier Palamara is associate professor at the University of Oxford's Department of Statistics and a group leader at the Wellcome Centre for Human Genetics. His research group develops statistical and machine learning algorithms for the analysis of large genomic data sets.       Title: Inference of Coalescence Times and Variant Ages Using Convolutional Neural Networks
	Tal Pupko studied math and biology as an undergraduate. He received his PhD in Tel-Aviv University in 2001 and in 2003, he returned to Israel as a faculty member. He is a full professor of bioinformatics and molecular evolution from 2013. He is an associated editor in the journal Molecular Biology and Evolution. Tal’s research is focused on various fields of molecular evolution and bioinformatics and combines methods from machine-learning, statistical inference, phylogenomics and microbial pathogenesis. Title: Harnessing AI in phylogenomics

The full program is available here.

• Alexandros Stamatakis,Institute of Computer Science, Foundation for Research and Technology Hellas Crete lab Heidelberg lab 
• Aurelien Tellier, Technical University of Munich
• Bastien Boussau, CNRS, Université Lyon 1
• Flora Jay, CNRS, Université Paris Saclay
• Franz Baumdicker, Universität Tübingen
• Laurent Jacob, CNRS, Sorbonne Université 
• Nikos Alachiotis , University of Twente
• Theano Apostolidi, Institute of Computer Science, Foundation for Research and Technology Hellas

We welcome abstracts of up to 300 words for presentations and posters.

Submission link.

Costs: 70 EUR registration fee for everybody (except keynotes)


contact email: stamatak@ics.forth.gr

Abstract submission deadline for oral presentations: February 21st, 2024 [closed]

Decision for oral presentations: March 10th, 2024

Abstract submission deadline for posters: April 1st, 2024 [closed]

Decision for posters: April 8th, 2024

Registration Deadline: April 15th, 2024 until 23:59 Central European Time [closed]

Interested participants are welcome to register and just attend the conference without presenting or submitting abstracts, however they will have to pay for accommodation on their own. 

Registration Fee Payment Deadline: April 30th, 2024

Payments via credit card is possible at https://hera.forth.gr/e-pay/registration.php.

Alternatively, you can pay via IBAN transfer - please send an email to the organizers for receiving the account data as we will not post them here.

Conference: May 13-15th, 2024

(freely adapted from NeurIPS and ICML)

The LEGEND conference aim at gathering the international community in
machine learning for evolutionary genomics, in order to foster an
exchange of ideas and a respectful scientific debate.

Our objective is that anyone interested by these events can attend
them comfortably, and in particular without experiencing any form of
harassement or discrimination (racist, sexist, homophobic, transphobic
or ableist).

Any witness or victim of such incidents or any other non-professional
behavior can get in touch with the organizers. All complaints will be
handled as confidentially as possible.

This workshop is co-funded by:

• the "Strengthening Computational Biodiversity Research in Greece" (Comp-Biodiv-GR) project under the auspices of the EU ERA Chair (HORIZON-WIDERA-2022-TALENTS-01: 2023-2028) program under project ID 101087081,
• the Agence Nationale de la Recherche project PIECES ANR-20-CE45- 0017