Summary
Abby Russo-Mendoza is a research scientist and neuroscientist based in San Francisco with nine years of experience applying machine learning, dynamical systems, and large-scale data analysis to motor control, learning, and decision-making. She bridges rigorous academic research—from a Columbia PhD and a Princeton postdoc—to industry R&D at Meta and CTRL-labs, building multi-network RNNs and end-to-end data pipelines that support complex neural recordings. Skilled in MATLAB, Python, TensorFlow, DataJoint, SQL and Tableau, she combines quantitative modeling with an artistic aesthetic for compelling data visualization and science communication. Abby has led interdisciplinary teams and product-minded projects, shrinking wearable medical-device footprints through energy- and algorithmic-optimization while coordinating engineers and scientists. Her work frequently blends hypothesis-driven neuroscience with practical ML tooling, revealing how population dynamics map onto behavior and hardware constraints—an approach that informs both fundamental science and deployable neurotechnology.
9 years of coding experience
5 years of employment as a software developer
Summer Course Methods in Computational Neuroscience, Summer Course Methods in Computational Neuroscience at Woods Hole Oceanographic Institution
Artificial Intelligence Professional Program, Artificial Intelligence Professional Program at Stanford University School of Engineering
Doctor of Philosophy - PhD Neuroscience, Doctor of Philosophy - PhD Neuroscience at Columbia University
Bachelor of Science (BS) Psychology, Bachelor of Science (BS) Psychology at Brandeis University