Cameron R. Wolfe PhD Student in Machine Learning, Rice University

About Me

Professional Headshot

I am a PhD student in Computer Science at Rice University in Houston, TX. I am currently advised by Dr. Anastasios Kyrillidis. My interests are are loosely related to math and machine learning. Currently, my main interests include non-convex optimization theory, multi-modal deep learning, graph neural networks, distributed neural network training, and quantum computing. This list is definitely not exhaustive, however, as my interests change and expand as I find new and interesting projects.

Prior to coming to Rice, I was an undergraduate student in Computer Science at UT Austin. During my time at UT, I did research alongisde the neural networks research group. My research interests were mostly related to genetic algorithms, and I was advised by Dr. Cem C. Tutum.

Publications

REX: Revisiting Budgeted Training with an Improved Schedule

Currently under review.

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Momentum-inspired Low-Rank Coordinate Descent for Diagonally Constrained SDPs

Currently under review.

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ResIST: Layer-Wise Decomposition of ResNets for Distributed Training

Currently under review.

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GIST: Distributed Training for Large-Scale Graph Convolutional Networks

Currently under review.

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Distributed Learning of Deep Neural Networks using Independent Subnet Training

Currently under review.

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Demon: Momentum Decay for Improved Neural Network Training

Currently under review.

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E-Stitchup: Data Augmentation for Pre-Trained Embeddings

Accepted as Undergraduate Honors Thesis, UT Austin (2020).

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Functional Generative Design of Mechanisms with RNNs and Novelty Search

Accepted as a Conference Paper at GECCO (2019).

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Tutorials

Introduction to Graph Convolutional Networks

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Introduction to AdaScaleSGD

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