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Michael Cho
Ph.D. Candidate, Applied ML Scientist, Researcher, Engineer
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Cylindrical Geodesic Flow Matching for Quasiperiodic Physiological Signal Transformation
NeurIPS 2026 Main Conference, Sydney. This work introduces a flow matching method for paired cardiovascular waveform translation that replaces the standard affine path with a closed-form geodesic on the phase-amplitude cylinder, outperforming interpolation baselines on zero-shot photoplethysmography and limited-support seismocardiography adaptation benchmarks.
Towards Monitoring Acute Opioid Withdrawal Severity Using a Multimodal Wearable Chest Patch
ACM Transactions on Computing for Healthcare. This study characterizes 32 cardiorespiratory features from wearable signals in 16 individuals with opioid use disorder over 7 days, identifying features that differ between high and low withdrawal and classifying withdrawal severity with machine learning.
EffSCG: Real-Time Seismocardiogram Denoising on Edge Devices
IEEE Journal of Biomedical and Health Informatics. This work presents an optimization framework that lets a deep learning seismocardiogram denoiser run directly on resource-constrained wearables, cutting inference time and memory footprint without substantive accuracy loss.
SCRIBE: Simultaneous Cardiac and Respiratory Impedance Biomarkers with Electromechanics
IEEE Sensors Journal. This work presents SCRIBE, a compact, flexible chest patch that concurrently measures cardiac and respiratory mechanics using ECG, seismocardiography, photoplethysmography, and bioimpedance.
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