Jason Kim
@jason_z_kim
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Postdoctoral researcher at Cornell interested in representation and computation in latent spaces of biological and artificial neural networks.
Joined July 2010
Ever wanted a low-dimensional model of your data that you could be confident would explain data structure and accurately re-embed out-of distribution data, all with minimal distortion of the geometry? Now you can with Γ-VAE! demonstrated on gene data.
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Working to understand the physics of how the brain works? Interested in understanding how brain function and collective dynamics emerge from neural interactions? Submit an abstract to our focus session "Statistical and Dynamical Physics of the Brain", APS2025 w/@ChrisWLynn!
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RT @NatureProtocols: #FeaturedProtocol this week is for a Python-based #software package to apply network control theory to the #humanconne…
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RT @LindenParkes: Our protocol paper for NCT is now online at @NatureProtocols!! Check it out here: @jason_z_kim @…
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@GeroldBaier Yes! Currently refactoring the code to make it usable and not one giant block of python code... Will update soon!
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In summary, by accurately preserving the manifold tangent spaces in low-dimensional embeddings, we better preserve the geometry of the data, make our embeddings more interpretable, make trustable models with great out-of distribution generalization, and uncover biology.
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RT @apd_flynn: 🚀 Exciting news alert! 🚀 Christoph Räth (@DLR_de) and I are thrilled to announce that our minisymposium proposal, 'Dynamic…
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@kevinmcld @jgvfwstone @LindenParkes @bart_larsen Ah, then we are in complete agreement! This dynamical integration with software and hardware is precisely the true vision. The analogy to computers is just a rhetorical device for broad communication, nothing more. Your statement is precisely what makes the brain so interesting!
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