I am Logan Sizemore

Machine Learning Researcher

Computer Science Educator

I am a PhD student in Artificial Intelligence and Computer Science at Oregon State University, where I work on machine learning for quantitative ecology and sustainability. My background is in applying machine learning to problems across astronomy, climate science, education, and sustainability. My recent work has included projects in stellar modeling, global energy system emulation. I am interested in quantifying uncertainty in ML for science, and bringing interpretable ML techniques to science domains. Before starting my PhD, I spent two years as an instructor at Western Washington University, where I taught courses in computer science, data science, cyber security, and computing education.

My Work

Gaia Net

Gaia Net is a pipeline for processing Gaia XP spectra for 220 million stars, estimating stellar parameters across all evolutionary stages and leading to the discovery of a new star-forming region, Ophion.

BOSS Net

BOSS Net is the default parameter pipeline for SDSS-V, designed to determine stellar parameters directly from optical and near-infrared spectra. It is a data-driven neural network that performs label transfer to provide fast, self-consistent stellar characterization across large survey datasets.

Skeletonkey

skeletonkey is a simple, lightweight, and flexible configuration management tool that allows you to manage complex configurations for your applications using YAML files. It dynamically loads classes and their arguments at runtime, making it easy to set up and modify your projects.

Chemistry Cardsort

We explore how students organize their knowledge by analyzing a chemistry card sort task using unsupervised learning techniques. We identified nuanced organizational strategies and differences between novice and expert students from the natural language justifications associated with each student's sort.

GCAM Emulation

The Global Change Analysis Model (GCAM) simulates interactions between Earth and human systems, offering insights into the co-evolution of land, water, and energy sectors under various scenarios. To enhance efficiency in large-scale simulations, a neural network emulator was trained to predict GCAM outputs with high accuracy.

• Kounkel, M., Sizemore, L., Shen, H. M., Chandler, N., Reneau, N., Pourlotfali, I., ... & Stassun, K. (2026). Probabilistic neural network approach to determining parameters of eclipsing binaries. arXiv preprint arXiv:2604.01281. (Accepted Astronomical Journal)
• Pallathadka, G. A., Aghakhanloo, M., Aird, J., Almeida, A., Amrita, S., Anders, F., ... & Runnoe, J. (2025). The Nineteenth Data Release of the Sloan Digital Sky Survey. arXiv preprint arXiv:2507.07093. (Accepted Astropysical Journal)
• Holmes, A., Shen, H. M., Jensen, M., Coffland, S., Sizemore, L., Bassetti, S., ... & Hutchinson, B. (2026). Emulating the Global Change Analysis Model with deep learning: An energy sector case study. Environmental Modelling & Software, 106945.
• Huson, D., Cowan, I., Sizemore, L., Kounkel, M., & Hutchinson, B. (2025). Gaia Net: Toward Robust Spectroscopic Parameters of Stars of all Evolutionary Stages. The Astrophysical Journal, 984(1), 58.
• Qiang, H., Kounkel, M., Bass, S., Lingg, R., Sizemore, L., Carroll, D., ... & Stassun, K. G. (2025). A Spatiotemporal Data Cube Approach to Classification of Variable Stars: A Catalog of Candidate Variable Stars from the TESS Full-frame Image Raw Data. The Astrophysical Journal, 984(1), 49.
• Sizemore, L., Llanes, D., Kounkel, M., Hutchinson, B., Stassun, K. G., & Chandra, V. (2024). A self-consistent data-driven model for determining stellar parameters from optical and near-infrared spectra. The Astronomical Journal, 167(4), 173.
• Holmes, A., Jensen, M., Coffland, S., Mitani-Shen, H. M., Sizemore, L., Bassetti, S., Nieva, B., Tebaldi, C., Snyder, A., & Hutchinson, B. (2024). Emulating the Global Change Analysis Model with deep learning. In Tackling Climate Change with Machine Learning Workshop at NeurIPS 2024.
• Hutchinson, B., Holmes, A., Jensen, M., Coffland, S., Shen, H. M., Sizemore, L., Seth Bassetti, Brenna Nieva, Abigail Snyder & Tebaldi, C. (2024). Emulating the Global Change Analysis Model with Deep Learning. AGU24.
• Sizemore, L., Hutchinson, B., & Borda, E. (2024). Use of machine learning to analyze chemistry card sort tasks. Chemistry Education Research and Practice, 25(2), 417-437.
• Wolters, P., Sizemore, L., Daw, C., Hutchinson, B., & Phillips, L. (2021). Proposal-based few-shot sound event detection for speech and environmental sounds with perceivers. arXiv preprint arXiv:2107.13616.
• Beck, J. P., Muniz, M. N., Crickmore, C., & Sizemore, L. (2020). Physical chemistry students' navigation and use of models to predict and explain molecular vibration and rotation. Chemistry Education Research and Practice, 21(2), 597-607.

• Google Scholar

Western Washington University

WWU CS Faculty of the Year, 2024-2025