Professional work

Medical-device engineering with range across hardware, sensing, data, and validation.

My professional work centers on wearable and implantable sensing systems, device reliability, clinical and home monitoring workflows, sensor integration, testing, documentation, and cross-functional execution.

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Primary domain Medical devices and wearable sensing
Practical range Hardware, validation, data, documentation, and systems execution

Work history

Roles, responsibilities, and outcomes.

Senior R&D Engineer

Rhaeos, Inc. · May 2022 – Dec 2025

Medical-device engineering role across FlowSense Clinical, FlowSense Home, algorithm development, manufacturing support, and FDA-ready documentation.

Worked on sensing systems that connected hardware, software, data, documentation, and real-world use conditions.

Responsibilities
  • Supported FlowSense Clinical / ACE and FlowSense Home / Lynx development.
  • Integrated sensors, electronics, packaging, adhesives, wireless charging, and test workflows.
  • Built validation workflows across bench testing, field use, clinical/home monitoring, and data review.
  • Created engineering documentation including requirements, test procedures, travelers, inspection records, and design notes.
  • Worked across hardware, firmware, software, manufacturing, quality, and clinical-facing workflows.
Accomplishments
  • Improved field reliability from roughly 70% to 96%.
  • Helped reduce 100-device build cycle time from roughly four weeks to about one week.
  • Supported 3,000+ hours of device/data collection.
  • Worked across 200+ datasets for sensor review, algorithm evaluation, and device-performance analysis.
  • Contributed to home-use and clinical-use device workflows.
Medical devicesWearable sensingFlowSenseValidationReliabilitySensor integrationDocumentationManufacturing supportData workflows

Graduate Research Assistant

Gutruf Lab, University of Arizona · Dec 2018 – May 2022

Ph.D. research focused on fully implantable wireless and battery-free bioelectronics for neural, skeletal, and physiological monitoring.

Designed implantable wireless platforms using flexible electronics, wireless power, communication, and preclinical validation.

Responsibilities
  • Developed wireless battery-free photometry and stimulation systems.
  • Built flexible, soft, and biocompatible implantable platforms.
  • Supported preclinical validation in freely moving animal models.
  • Worked across power transfer, communication, encapsulation, and mechanics.
Accomplishments
  • Advanced a dissertation on fully implantable wireless and battery-free organ interfaces.
  • Built systems for chronic sensing and stimulation without tethered batteries.
ImplantablesWireless powerFlexible electronicsEncapsulationPreclinical validation

Research Technician

EUNIL / University of Arizona · Jan 2016 – Dec 2019

Research role centered on non-invasive neural recording, ultrasound-modulated current-density detection, amplifier optimization, EMI control, and DSP methods.

Developed signal-quality and experimental workflows for neural sensing and acoustoelectric measurement systems.

Responsibilities
  • Worked on non-invasive neural recording experiments and related phantoms.
  • Improved amplifier performance and EMI/noise control.
  • Used wavelet and DSP methods to improve signal interpretation.
  • Supported experimental setups for current-density detection research.
Accomplishments
  • Built practical experience in low-noise sensing and experimental debugging.
  • Strengthened the bridge between signal quality, hardware setup, and analysis.
Neural recordingSignal processingAmplifier optimizationEMI controlPhantoms

Data Analyst

iCAMP Research Group, University of Arizona · Sep 2014 – Jan 2017

Early physiological-sensing role focused on chest-worn sensor data, environmental stress, fall-risk/frailty studies, calibration, labeling, and prediction workflows.

Built a foundation in noisy human physiological data, sensor calibration, and early predictive analysis.

Responsibilities
  • Supported chest-worn physiological sensor studies and data preparation.
  • Worked on calibration, labeling, and prediction workflows for human sensor data.
  • Analyzed environmental stress and fall-risk / frailty signals.
  • Helped structure real-world time-series data for downstream analysis.
Accomplishments
  • Built an early base in physiological sensing and human-data analysis.
  • Learned how sensor placement, labeling, and calibration affect downstream signal quality.
Physiological sensingData labelingCalibrationPrediction workflowsTime-series analysis

Metrics

Public proof points from engineering work.

Reliability improvement

70% → 96%

Build cycle improvement

4 weeks → ~1 week

Data collection

3,000+ hours

Datasets reviewed

200+ datasets

Capabilities

Skills demonstrated through the work.

Medical-device development Wearable sensing Flexible electronics FlowSense Clinical/home-use monitoring Sensor integration Adhesives and skin interface Encapsulation Wireless power BLE / NFC Validation and verification Algorithm validation Thermal transport Physiological signal processing Reliability debugging Manufacturing support Documentation Data analysis AI tooling Self-hosted infrastructure