2016 - BSc, Applied Mathematics, St. Petersburg, Russia.
2018 - MSc, Algorithmic Bioinformatics, St. Petersburg, Russia.
Present - PhD, Computer Science, St. Petersburg, Russia.
Currently live in: Zürich, Switzerland
drawn by demes [Gower et al. 2022]
Understand population history
Conservation biology studies
for $\partial a \partial i$
Most tools use local search optimization algorithms:
They require initial estimation and perform search for local optimum.
GADMA — Global search Algorithm for Demographic Model Analysis
New model in GADMA that is specified only by the number of epochs.
Available up to three populations
New model in GADMA has flexible dynamics of population size change.
Population dynamic can be:
GADMA implements a combination of the genetic algorithm followed by a local search method.
Hyperparameters of the genetic algorithm are optimized (SMAC).
Machine learning-based technique for optimizing expensive functions.
Enables demographic inference for 4 and 5 populations in GADMA.
History obtained by GADMA has better likelihood and CLAIC.
$\partial a \partial i$
Ground truth: 2,200
History obtained by GADMA has better likelihood.