From physiological data to implantables to medical-device systems.
A chronological view of the technical work: sensor data, low-noise
electronics, wireless implantables, wearable medical devices, validation,
documentation, and systems that have to work outside the lab.
iCAMP Research Group, University of Arizona · Sep 2014 – Jan 2017
Data Analyst
Built a foundation in noisy human wearable data, ECG/HRV feature extraction, accelerometer calibration, labeling, and predictive modeling support.
Role focus
Scope of role
Analyzed chest-worn ECG and acceleration data from wearable physiological studies.
Performed ECG filtering, R-wave detection, R-R interval correction, and HRV feature generation.
Calibrated and reoriented 3D acceleration data for posture/activity classification.
Supported wearable-data feature extraction for environmental stress and fall-risk/frailty modeling.
Achievements
Evidence from the work
Extracted ECG, HRV, posture, and activity features from 800+ hours of chest-worn wearable data across 31 subjects.
Built wearable physiological-data workflows spanning filtering, feature generation, calibration, labeling, and prediction support.
EUNIL / University of Arizona · Jan 2016 – Oct 2019
Research Technician
Designed and debugged low-noise neural-recording hardware, 4D acoustoelectric imaging instrumentation, phantoms, and DSP workflows for current-density detection.
Role focus
Scope of role
Designed and debugged low-noise neural-recording hardware, amplifiers, interconnects, and bench instrumentation.
Simulated electrode current densities and supported 4D acoustoelectric imaging workflows.
Built tissue-equivalent phantoms and controlled experimental setups for sensing and recording studies.
Improved signal fidelity through circuit optimization, grounding, shielding, EMI control, DSP, wavelet processing, and MATLAB analysis.
Achievements
Evidence from the work
Built and optimized front-end hardware, phantoms, and signal-processing workflows for 4D acoustoelectric current-density imaging.
Presented the non-invasive neural-recording work at BMES 2017 and IEEE IUS 2018.
Gutruf Lab, University of Arizona · Dec 2018 – May 2022
Graduate Research Assistant
Designed and validated miniaturized implantable systems across wireless power, flexible interfaces, optical photometry, encapsulation, communication, stimulation, and preclinical workflows.
Role focus
Scope of role
Developed wireless battery-free photometry, neurostimulation, osseosurface sensing, and high-power FES platforms.
Integrated rigid electronics with flexible probes, serpentine interconnects, soft packaging, and biocompatible encapsulation.
Built bench-validation infrastructure for antenna/power tuning, output characterization, fixture design, accelerated aging, and packaging validation.
Supported preclinical validation in freely moving small-animal models where device mass, packaging, wireless reliability, and surgical handling controlled study quality.
Achievements
Evidence from the work
Developed fully implantable wireless and battery-free platforms for neural recording, neurostimulation, musculoskeletal monitoring, and functional electrical stimulation.
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.
Supported miniaturized implant platforms below 50 mg and up to 2 m wireless power range in source-review framing.
Mentored or managed 10+ researchers/students across multidisciplinary implantable-device development tasks.
Led technical work across wearable hardware, sensor integration, home/clinical data workflows, reliability, ML validation, and regulated documentation.
Role focus
Scope of role
Supported FlowSense Clinical / ACE, FlowSense Home / Lynx, and Wound Monitoring Platform / Tabby development.
Integrated sensors, electronics, packaging, adhesives, Qi charging, motion sensing, onboard memory, and data workflows.
Built validation workflows across bench testing, clinical/home monitoring, preclinical studies, data review, and deployment checks.
Created FDA-ready engineering documentation including requirements, test protocols, assembly procedures, inspection records, BOMs, DMFEA, and design notes.
Worked across hardware, firmware, software, clinical, regulatory, manufacturing, supplier, patient, and caregiver feedback loops.
Achievements
Evidence from the work
Improved FlowSense Home field reliability from about 70% in Gen 1 to about 96% by the final Gen 3 clinical study.
Led 20–30 clinical-study device builds and reduced build cycles from about 6 weeks to about 3 weeks.
Advanced FlowSense Home across 3 design generations, 100+ manufactured units, 200+ participants, and 2,800+ home-device wear hours.
Supported 3,000+ hours of physiological/device data and 200+ HA Connect datasets.
Built DSP/ML workflows spanning ingestion, labeling, QC, dataset versioning, feature generation, validation, bias checks, explainability, and deployment checks.
Helped improve FlowSense clinical algorithm performance from a 74% baseline to 0.81 AUC / 82% accuracy in blinded clinical validation.
Led NIH R43 Phase I wound-sensing work from concept to study-ready device and grant deliverables in about 1 year.
Technical range
Tools, systems, and technical areas
Medical devicesWearable sensingFlowSenseAlgorithm validationReliabilitySensor integrationAdhesives and skin interfaceQi chargingBLE / NFCManufacturing readinessFDA-ready documentationPhysiological ML