Summary
Sergio Manso is a biomedical engineer and software-savvy neuroengineer with nine years of experience building brain-computer interfaces, neuroprosthetic decoding pipelines, and ML-driven gait analysis tools. Currently a graduate researcher at UT Austin’s Clinical Neuroprosthetics Lab, he focuses on noninvasive BCI for fine hand motor decoding using foundation models while collaborating on invasive sleep-stage classification and nerve-stimulation modeling at MIT and Johns Hopkins. He brings a rare blend of experimental, computational, and embedded-systems expertise—from EEG/EMG gait datasets and autonomous gait-segmentation apps to I2C sensor firmware and TIRS algorithms for the Mars2020 mission. Sergio’s work consistently bridges academic rigor and practical tools, demonstrated by deep learning models that surpassed 90% accuracy in gait-phase tasks and production-ready apps for kinematic analysis. Based in Austin, he pairs ongoing PhD-level ECE training with dual-degree graduate studies and hands-on software development (including Flutter), enabling rapid translation of lab prototypes into usable systems.
9 years of coding experience