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Makes every class a rewarding experience.
Creates a safe space for learning and growth.
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I deeply appreciate how supportive you were throughout the course. You always made time to answer questions and provide guidance when I needed it most.
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About Theodore
Theodore J. LaGrow serves as a Lecturer in the FlexStack Program within Georgia Tech's College of Lifetime Learning and as the Instructor for CS 7641: Machine Learning in the College of Computing. Affiliated with the School of Electrical and Computer Engineering and a faculty member in Bioengineering, he completed his PhD in Electrical and Computer Engineering at the Georgia Institute of Technology in 2025, following his MS, MBA, and Associate degrees from the same institution. His undergraduate studies were at the University of Oregon, where he majored in Mathematics, Computer Information Systems, Physics, and Theater. LaGrow has instructed courses at Georgia Tech for nearly a decade, leading large-scale offerings such as CS 7641 for over 1,200 students. He also developed and teaches the Data Modeling and Visualization Certificate, covering pandas, matplotlib, and scikit-learn, as well as the Python AI Principles Certificate, where students construct functional text chatbots using large language models.
LaGrow's research specializes in computational neuroscience, signal processing, brain network dynamics, and fMRI analysis, with a focus on spatiotemporal dynamics in neurodegenerative functional brain networks, including Alzheimer's disease progression. His Google Scholar profile lists over 226 citations, reflecting impact in the field. Key publications include first-author works such as "Approximating Cellular Densities from High-Resolution Neuroanatomical Imaging Data" (2018, Annual International Conference of the IEEE Engineering in Medicine and Biology Society) and "Spatiotemporal Network Dynamics Reveal Alzheimer's Disease Progression" (bioRxiv, 2025 preprint). Notable co-authored papers are "Functional Connectivity of the Brain Across Rodents and Humans" (Frontiers in Neuroscience, 2022, 125 citations), "QPPLab: A generally applicable software package for detecting, analyzing, and visualizing large-scale quasiperiodic spatiotemporal patterns (QPPs) of brain activity" (SoftwareX, 2025, 12 citations), "Voxel-wise Fusion of Resting fMRI Networks and Gray Matter Volume for Alzheimer’s Disease Classification using Deep Multimodal Learning" (Research Square, 2023), and "Spatial and Spectral Components of the BOLD Global Signal in Rat Resting-State Functional MRI" (Magnetic Resonance in Medicine, 2023). Additionally, he co-founded a medical imaging startup and is an inventor on a patent for diagnosable image capture and storage.
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