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Hi. I'm Chris Henn.

[email protected]

I'm an MIT alum (grad 2020) with ~2 years' experience in machine learning research at MIT's Center for Brains, Minds, and Machines. I also have professional experience in performance software design and engineering, data modeling and analysis, mechanical systems simulation, and electronic systems fabrication.

To solve problems, I enjoy starting from scratch. Computer science is a powerful tool: a way of turning time, money, and know-how into effective solutions. I try to remember that every problem starts to look like a nail, once I've wielded the computer as a hammer long enough. Effective solutions always include relying on the knowledge, time, and the people at hand - not just the available computational power - moving focus onto communication, in addition to engineering excellence. As much as I like starting from scratch, I try to ask: am I inventing a new way to solve a solved problem? Am I solving a problem that doesn't need to be solved?

In that pursuit, I've written: webclient code in Javascript; GPU libraries in CUDA C and CUDA-python; GUI interfaces in C#; parallel, asynchronous, and concurrent code in python, C#, C++, and Java; docker setup scripts, apptainer (singularity-container) setup scripts, and CI testing configurations in bash; a python-based web server; a mechanical model in MATLAB; ML models on AWS and MGHPCC; 3D designs in fusion 360; and built with Pytorch, Tensorflow, gcc, nvcc, Numba, Numpy - whatever fits the problem at hand.

For the past ~2 years, I've been employing these tools to design, evaluate, and interact with, novel computer vision model-architectures - and investigate hypotheses about the fundamental structure of image data (and by extension, how we model the structure of data in general).

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