A central challenge in biology is understanding how organisms evolve and adapt to their environment, acquiring variations in observable traits across the tree of life. However, measuring these traits is often subjective and labor-intensive, making trait discovery a highly label-scarce problem. With the advent of large-scale biological image repositories and advances in generative modeling, there is now an opportunity to accelerate the discovery of evolutionary traits. This talk focuses on using generative models to visualize evolutionary changes directly from images without relying on trait labels.
ECCV 2024 Paper: Hierarchical Conditioning of Diffusion Models Using Tree-of-Life for Studying Species Evolution
About the Speaker: Mridul Khurana is a PhD student at Virginia Tech and a researcher with the NSF Imageomics Institute. His research focuses on AI4Science, leveraging multimodal generative modeling to drive discoveries across scientific domains.
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