Lucas Hayne
Senior Machine Learning Engineer @ Matterport
About
I am Machine learning PhD student at CU Boulder. My research develops methods for analyzing neural data from both artificial and biological neural networks. These methods can be used to improve neural network training and interpretability by elucidating the connection between neural network representations and performance. In addition, I have collaborated on deep learning projects for image compression, brain computer interfaces, and deep learning scaling law development.
United States
Boulder
Higher Education
Organization Skills, Oral Communication, Problem Solving, Communication, TensorFlow, PyTorch, Neuroimaging, Functional Near Infrared Spectroscopy, NumPy, Neural Networks, Optimization, Neural Network Interpretability, Model Compression, Deep Learning, Computer Graphics, Artificial Neural Networks, Bokeh Visualization Library, Statistical Data Analysis, Supervised Learning, University Lecturing
Experience
Senior Machine Learning Engineer
Matterport
Graduate Researcher
University of Colorado Boulder
Boulder, Colorado, United States
— First-authored “Most Well Written Paper” at NeurIPS I Can’t Believe It’s Not Better Workshop 2022. — Trained or analyzed ViT, ResNet, MobileNet, VGG, AlexNet, MLP-Mixer on CIFAR, ImageNet on multiple GPUs. — Enhanced representation similarity metrics (CKA, Procrustes, PWCCA) for 100x memory cost reduction.
Graduate Teaching Assistant
University of Colorado Boulder
Boulder, Colorado, United States
— Neural Networks and Deep Learning (two sections, 100 students, Graduate) — Probability for Computer Science (one section, 20 students, Graduate) — Algorithms (one section, 40 students) — Introduction to Programming (two sections, 50 students) — Cognitive Science (one section, 80 students)
Data Analyst
National Laboratory of the Rockies
Golden, Colorado, United States
— Systematically reviewed effects of neural network topology on learning dynamics and performance. — Co-authored publication of 671 GB open-source dataset on neural network optimization by contributing literature review, writing, and data visualization. — Identified scaling laws in finite data regime for common ML datasets. — Writing collaborative software, conducting experiments, analyzing data, generating figures, literature reviews, and writing results.
SIParCS Graduate Intern
NSF NCAR - The National Center for Atmospheric Research
Boulder, Colorado, United States
— Achieved 100% improvement over SOTA compression algorithms for scientific data using neural network image compressors. — First-authored IEEE Big Data publication and presented findings at IEEE Big Data 2021 Conference.
Education
University of Colorado Boulder
Computer Science
— Graduate School Summer Research Fellowship 2023 ($6,000) — Outstanding TA Recognition Award 2023
University of Colorado Boulder
Computer Science
University of Colorado Boulder
Computer Science
— Summa Cum Laude — Certificate in Cognitive Science — Dean’s List Fall 2013 – Fall 2016 — Phi Beta Kappa Member — Imogene Jacobs Award Outstanding Honor’s Thesis in Psychology
Lucas Hayne's Contact Information
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