Bio

Following a 2016 bachelor’s in neuroscience, mathematics and computer science, Sumner Magruder utilized his Boehringer Ingelheim Fonds grant to study neurodegenerative diseases in Germany. There, his work on graph theory, deep learning and visualization—with the support of Nvidia—simultaneously earned him a master’s equivalence in machine learning. He currently focuses on generative adversarial models and software development.

Computational Essay

Parts of Sets »

Project: A Deviation from “Try to Use Neural Nets for Graph Isomorphism”

Goal

Find an input form of graphs that is sufficient for neural networks to learn on.

Main Results in Detail

Despite sparsity, it seems that the adjacency matrix is sufficient for learning complex and even arbitrary properties of graphs.

Future Work

• 1. Explore other input formats.
• 2. Export other graph properties.