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Simone Heo

@simoneheo

Research engineer specializing in Python-based machine learning for wearable physiological sensing and deep-learning-driven intelligent systems.

United States

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What I'm looking for

I’m looking for a role where I can apply Python and deep learning to physiological sensing data, collaborate across multidisciplinary teams, and take prototypes through validation—turning research insights into clinically meaningful wearable systems.

I’m a research engineer with a Ph.D. and 8+ years of experience developing wearable electronics and analyzing physiological data. I’m proficient in Python and focused on building machine learning models—especially deep learning—for intelligent sensing systems, from sensor integration through prototype development and validation.

In my consulting work at LungDx, I developed and evaluated machine learning models for neonatal lung sound classification, handling data preprocessing, feature engineering, model evaluation, and interpretation of experimental results. I also collaborated across hardware, firmware, software, and clinical teams to validate wearable respiratory monitoring systems through lab and clinical testing, and I led end-to-end development at HemoRhythmics as a Lead Research Engineer & Co-Founder, bringing wireless implantable sensing systems for continuous blood pressure monitoring and smart stent applications from concept through prototype evaluation. I’ve also built Python workflows for preprocessing and analyzing multimodal physiological data, developed supervised machine learning models to classify ECG signal quality using reference-aligned labels, and taught wearable electronics and embedded firmware lab experiences at Northwestern University—so I bring both research depth and a practical engineering mindset to teams.

Experience

Work history, roles, and key accomplishments

Consulting Engineer

LungDx

Jan 2024 - Jan 2025 (1 year)

Developed and evaluated machine learning models for neonatal lung sound classification, including data preprocessing, feature engineering, model evaluation, and interpretation. Collaborated with hardware, firmware, software, and clinical teams to validate wearable respiratory monitoring systems through laboratory and clinical testing.

Machine Learning data preprocessing Feature Engineering Model Evaluation Model Interpretability Wearable Respiratory Monitoring Laboratory Testing Cross Functional Collaboration

Visiting Researcher

Center for Bio-Integrated Electronics

Jan 2021 - Jan 2025 (4 years)

Developed Python workflows for preprocessing and analysis of multimodal physiological data from wearable sensing systems to evaluate sensor performance and identify anomalies. Built supervised machine learning models to classify wearable ECG signal quality using reference-aligned labels derived from Philips bedside monitors.

Python data preprocessing Sensor Performance Evaluation Supervised Learning Models ECG Signal Quality Classification Reference Aligned Labeling

Lead Research Engineer & Co-Founder

HemoRhythmics

Jan 2021 - Jan 2025 (4 years)

Led development of wireless implantable sensing systems for continuous blood pressure monitoring and smart stent applications from concept through prototype evaluation. Synthesized experimental results into technical reports and presentations and secured research funding through technical reviews with industry partners including Edwards Lifesciences.

Smart Stent Applications Prototype Development Technical Reporting Executive Briefings

Visiting Instructor

Northwestern University

Designed laboratory exercises on embedded firmware development, sensor interfacing, and data acquisition using Nordic nRF52832 platforms. Taught a project-based Wearable Electronics course covering biomedical sensing, embedded systems, and wireless device design for undergraduate and graduate engineering students.

Embedded Firmware Development Sensor Interfacing Data Acquisition Wearable Embedded Systems Teaching