D. D. Ray

IntroUNET: identifying introgressed alleles via semantic segmentation

This paper introduces IntroUNET, a deep learning method for identifying introgressed alleles using semantic segmentation, showing high accuracy in simulated and real data from Drosophila.

Tree sequences as a general-purpose tool for population genetic inference

The paper proposes a graph convolutional network to directly learn from tree sequences for population genetic inference, showing comparable or better performance than CNNs on benchmark tasks.

Mechanical positive feedback and biochemical negative feedback combine to generate complex contractile oscillations in cytokinesis

The paper shows that mechanical and biochemical feedbacks combine to create complex contractile oscillations during cytokinesis.

This population does not exist: learning the distribution of evolutionary histories with generative adversarial networks

This paper trains a GAN to generate artificial population genetic alignments that mimic key features of real data across diverse evolutionary models.