Yoel Araujo is a bioinformatician and computational neuroscience researcher with a decade of experience applying machine learning, statistics, and AI to neural data and behavioral modeling. He completed advanced research and teaching roles at Princeton—co-advised by leaders in probabilistic modeling and reinforcement learning—and has hands-on experience with multimodal imaging and ECoG decomposition from prior work at MIT and Rutgers. His current research bridges sensory decision-making and reinforcement learning to study de novo task acquisition, combining Bayesian nonparametrics and matrix factorization techniques to interpret complex neural signals. Based in Washington, D.C., he brings both deep theoretical training (PhD-level neuroscience) and practical pipeline-building skills for analysis of large neuroimaging and behavioral datasets. Colleagues describe him as an interdisciplinary problem-solver who turns abstract computational ideas into reproducible analyses and educational materials.
10 years of coding experience
5 years of employment as a software developer
Bachelor's Degree Psychology, Bachelor's Degree Psychology at Rutgers University–Newark
Advanced Study Program, Advanced Study Program at Massachusetts Institute of Technology
Doctor of Philosophy - PhD Neuroscience, Doctor of Philosophy - PhD Neuroscience at Princeton University
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