Michael McCabe

Michael McCabe

I am a Ph.D. student at the University of Colorado studying Machine Learning and Scientific Computing in the PhyPID group under Prof. Jed Brown

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

Using Differentiable Physics for Self-Supervised Assimilation of Chaotic Dynamical Systems

Published in Workshop on Differentiable Vision, Graphics, and Physics in Machine Learning at NeurIPS 2020, 2020

We propose a deep learning based data assimilation framework which we call \textit{Amortized Assimilation} for state estimation in high-dimensional chaotic dynamical systems. Amortized assimilators utilize differentiable simulation of physics-derived system dynamics to enable end-to-end physics-aware gradient based training of denoising neural networks which update a simulated system state based on noisy observations. These hybrid models are able to learn to assimilate complex input distributions while maintaining a computable test-time update step in an entirely self-supervised manner using only sequences of noisy observations without loss of accuracy over training with ground truth targets. Numerical experiments demonstrate that amortized assimilators compare favorably with widely used data assimilation methods across common benchmark tasks.

Recommended citation: Michael McCabe, Jed Brown. (2020). "Using Differentiable Physics for Self-Supervised Assimilation of Chaotic Dynamical Systems." Workshop on Differentiable Vision, Graphics, and Physics in Machine Learning at NeurIPS. https://montrealrobotics.ca/diffcvgp/assets/papers/16.pdf

talks

Graph Neural Networks in the Physical Sciences

Published:

Deep neural networks have contributed to wildly improved performance across a variety of tasks in fields like NLP and computer vision. One property of deep learning that gets credited for this progress is the ability to encoder prior knowledge of the problem structure into the architecture of the network. Unfortunately, the convenient low dimensional grid structure found in these problems that allows for straight-forward learning of locally compact filters is not present in the wide variety of non-Euclidean data used by other scientific fields which can often only be represented as graphs or point clouds. Graph Neural Networks, and in particular Graph Convolutional Neural Networks, are a family of architectures developed to address these types of problems. In this presentation, I’ll review the major ideas and milestones in the development of modern graph neural networks and highlight several recent applications in the physical sciences.

Data Science in the Healthcare Industry

Published:

From clinician-level analysis of medical imaging, to precision medicine powered by fine-grained genomic models, to automated drug discovery, it often seems like machine learning is only a few years away from completely transforming healthcare as we know it. While progress is certainly being made, in practice, the healthcare industry is an enormously complicated beast that is more resistant to change than one might expect from browsing arXiv. In this talk, I will discuss the structure of the healthcare industry, where organizations are currently deriving value from data science, and the complications that arise when trying to translate hype into improved care. We’ll explore a few use cases spanning both the clinical and business side of healthcare, focusing on problems shared by both large research institutions and smaller community hospitals and cover some specific technical and data challenges frequently faced by healthcare organizations as they start exploring data science.

teaching