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.

LinkedIn GitHub Contact
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 FlowSense Home field reliability from about 70% in Gen 1 to about 96% by the final Gen 3 clinical study.
  • Led 20 to 30 clinical-study device builds, reducing build-cycle timelines from about 6 weeks to about 3 weeks across sensor design, builds, encapsulation, testing, and documentation.
  • Supported 3,000+ hours of physiological/device data across Rhaeos sensing programs.
  • Worked across 200+ HA Connect datasets for sensor review, algorithm evaluation, and device-performance analysis.
  • Helped improve FlowSense clinical algorithm performance from a 74% Gen1/expert baseline to 0.81 AUC / 82% accuracy in blinded clinical validation.
  • Supported a multicenter clinical workflow across 9 hospitals, 182 subjects, and a 112-subject validation set.
  • Supported final FlowSense + imaging workflow framing with 92% sensitivity / 97% NPV.
  • Built wound-healing prediction analysis from 10 control and 10 diabetic mouse subjects using 5-fold cross-validation.
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
  • Built reusable validation infrastructure across 20+ test fixtures, 6 tuned antenna designs, and 3 simulation frameworks.
  • Contributed to peer-reviewed work in PNAS, Microsystems & Nanoengineering, and Nature Communications, with 14 peer-reviewed journal articles, 5 conference proceedings/abstracts, and 1 book chapter.
  • Mentored or managed 10+ researchers/students across graduate research work.
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
  • Extracted ECG, HRV, posture, and activity features from 800+ hours of chest-worn wearable data across 31 subjects.
  • Supported fall-risk classification improvements from 0.73 baseline AUC to 0.969 mixed-model AUC in the Biohub deck reference.
  • Developed a low-cost optical/microfluidic nanoparticle analyzer with about $4,500 prototype cost, below $5 chamber cost per unit, and below 1.9% size error across 50 nm to 1 µm particles.
Physiological sensingData labelingCalibrationPrediction workflowsTime-series analysis

Metrics

Public proof points from engineering work.

Biomedical sensing experience

11+ years

Commercial med-device programs

3+ years

FlowSense Home reliability

70% → 96%

Clinical-study build cycle

6 weeks → ~3 weeks

Clinical-study build size

20–30 devices

Physiological/device data

3,000+ hours

HA Connect datasets

200+ datasets

Clinical algorithm validation

0.81 AUC / 82% accuracy

FlowSense + imaging

92% sensitivity / 97% NPV

Clinical workflow scale

9 hospitals / 182 subjects / 112-subject validation set

Publication output

14 journal articles / 5 conference proceedings or abstracts / 1 book chapter

Implant validation infrastructure

20+ fixtures / 6 tuned antenna designs / 3 simulation frameworks

Early wearable-data study

31 subjects / 800+ hours

Fall-risk model reference

0.73 baseline AUC → 0.969 mixed-model AUC

Nanoparticle analyzer

~$4,500 prototype / <$5 chambers / <1.9% size error across 50 nm–1 µm

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