UX Researcher & Human Factors Data Engineer
AR & XR · Human Factors · User Research · Wearable Systems
About
I am a UX Researcher and Human Factors Data Engineer at Magic Leap, conducting human-centered research across rapid prototyping and hardware development environments for AR/XR head-mounted display systems. I work across Android XR and Magic Leap reference prototypes — currently driving a cross-organizational project with a partner company on glasses frame deflection and head phenotype development.
My work bridges behavioral UX research and physical human factors — contributing across study design, user interviews, data analysis, and synthesis as part of the research team, while owning scan analysis from capture through reporting. Across 2,000+ human subjects, 600+ scan alignments, and multiple device generations, I've developed new methods adopted across the research team and co-authored published research on nose shape and HMD design, with a second paper in progress. BBA from Florida Atlantic University. Certified in GCP for Human Subject Research.
Experience
Magic Leap
UX Researcher & Human Factors Data Engineer · Mar 2023 – Present · Plantation, FL
Mars Research
Market Research Intern · Jan 2023 – Mar 2023 · Fort Lauderdale, FL
Florida Atlantic University
BBA Business Administration, Management
Tools & Skills
Research Tools
PolyWorks Inspector · JMP · 3DMD Wrap · Vultus · Figma · Jira · Gemini · NotebookLM
Research Methods
Usability Testing · User Interviews · Survey Design · Contextual Inquiry · Diary Studies · Benchmarking · Competitive Device Evaluation · Journey Mapping · Persona Development
Analysis & Output
Anthropometric Analysis · Scan Alignment · Cross-sectional Analysis · PCA · JMP Statistics · API Data Pipeline · Data Visualization · Executive Reporting
Certifications & Publication
GCP for Human Subject Researchers
CITI Program
Nose Shape Categorization & HMD Research
Peer-Reviewed · Co-Author · 2024
Business Operations Specialist
Broward College
Business Specialist
Broward College
Peer-Reviewed Publication · Co-Author
Nose Shape Categorization & Its Impact on HMD Research
Evaluates how Martin & Saller's nasal index correlates with comprehensive nose shape variables using Principal Component Analysis (PCA) and nonparametric bivariate correlation analysis in the context of HMD design. Published in an ergonomics & HMD design proceedings, 2024. Co-authors: Tom Schnieders & Karen Bredenkamp.
A second paper on nose shape categorization is currently in progress — credit pending publication.
Portfolio · Part 1
A selection of UX research work across AR and XR. Due to NDA and confidentiality requirements, the case studies below represent a redacted cross-section of work that also spans AR navigation, spatial content placement, OS and home menu design, notification systems, display and dimming research, multiple device application experiences, voice and gesture interaction, and system communications across multiple device generations.
Platform Research
Android XR · Reference Prototypes
Platform & Device Research
UX research across AR/XR platforms including Android XR and Magic Leap reference prototypes — spanning interaction design, input modalities, OOBE and first-use flows, onboarding, and platform-level decisions across hardware and software layers.
Benchmarking
Device Benchmarking
Multi-round benchmarking studies comparing AR/XR interaction modalities and competitor hardware — spanning hand tracking vs. controller performance (SUS, perceived comfort, accuracy scales), hand anthropometry collection, arm and wrist fatigue assessment, and competitive device evaluation across industry-leading XR platforms.
AI-Assisted Research
AI Research Methods
Applied Gemini and NotebookLM for lit review, competitive analysis, and proposal drafting. Built an API-based data pipeline to extract and analyze user sentiment from public platforms at scale — delivered as written proposals to stakeholders.
Product OS & App Research
On-Device Experiences
Evaluative and generative research across on-device AR experiences — home menu and OS navigation, notifications and system communications, display and dimming, spatial content placement, app usability (capture, scanning, onboarding, developer apps), and AR map navigation. Work spans behavioral and attitudinal methods across both qualitative and quantitative approaches, informing product road maps and key design decisions.
Featured Case Study
Mixed-Method Benchmark · 28 Participants
A mixed-method benchmark of direct-interaction (hand-only) navigation across the full OS task suite, designed to answer a single product question: is hand tracking good enough to ship enabled by default? I contributed across protocol and rating-scale design, moderation, behavioral coding, and synthesis as part of the research team.
Method
Moderated, in-person · Mixed-method · Within-subjects task suite
Participants
28 external, native users · None-to-high prior AR/VR experience
Instruments
SUS · Per-task rating scales · Perceived comfort (pre/post) · Structured observation
Study Instrument — Per-Task Rating Scales
Discoverability
Ease of Use
Perceived Accuracy
Perceived Comfort
Outcome
Impact: The benchmark drove a conditional-ship recommendation and a prioritized redesign brief — depth-feedback cursor, virtual keyboard, and gesture instruction — delivered to product and engineering.
Additional Case Studies
Case Study 01 · Moderated Usability
AR Onboarding & First-Use Experience
Evaluated a multi-module AR onboarding experience across three input methods — direct interaction, indirect interaction, and controller — in six real-world environments ranging from private offices to open factory floors. 11 participants. Behavioral coding, task completion analysis, and qualitative debrief surfaced critical gaps in gesture instruction, depth feedback, and content visibility. Findings directly informed tutorial redesign priorities and ghost-nudge architecture delivered to the OS team.
Case Study 02 · Remote Diary Study
Longitudinal Wearable Experience
Multi-week remote, unmoderated diary study capturing the day-to-day lived experience of AR wear — perceived cognitive comfort, mental load, social acceptability, and perceived physical comfort tracked over time. Participants self-reported through structured prompts and surveys in their own environments. I contributed activity design, survey instrumentation, data cleaning, longitudinal synthesis, and stakeholder reporting. The unmoderated, in-context format surfaces adoption and comfort signals that lab sessions can't capture.
Case Study 03 · Comparative Evaluation
Clipping Plane: End-User & Developer Study
37 external participants evaluated three clipping plane conditions — Baseline (hard clip), Conservative (near-fade), and Liberal (no clip) — across three real-world AR tasks: spatial object search, direct UI interaction, and close-range text reading. Within-subjects design with preference ratings and structured qualitative debrief. Findings revealed nuanced user preferences around content solidity, perceived movement, and perceived visual comfort across conditions. Outputs were formatted as end-user and developer guidance published to the AR developer portal.
Portfolio · Part 2
Our scan analysis process begins with in-person collection in our lab. The team runs anthropometry on each participant — landmarking key facial points by palpation, then capturing baseline and fit scans. After the session, I digitize those landmarks in PolyWorks, determine coordinate points, and measure the deltas between imported and aligned positions. Physical collection is imperative — scan analysis is what allows us to investigate findings further, giving the team extended time with a participant's anatomy long after they've left the building.
This generates findings that engineering and industrial design teams act on directly — verifying comfort and slippage observations from the live session, evaluating visual registration more accurately than in-person proctoring allows, and producing measurements that feed mechanical engineering decisions like clamp force.
This work directly supports decisions around physical comfort, slippage mitigation, and visual registration — the critical factors that determine whether an AR device can be worn all day by a diverse global population.
Scan alignment & analysis — presenting to cross-functional stakeholders
Additional Work — Device Fit & Comfort Studies
Beyond scan analysis, I contribute to large-scale physical fit and comfort studies evaluating AR/XR HMDs across diverse participant populations. Our team draws on a 2,000+ person anthropometry database capturing 19 traditional 1D measurements, 18 facial landmarks, and sub-millimeter-accurate 3D head scans across Asian, Caucasian, and African-American demographics — providing the statistical foundation for fit validation at population scale. In-person sessions combine anatomical landmarking by palpation (glabella, tragion, nasion, infraorbitale, zygion, and others), traditional anthropometric measurements (head breadth, head length, face breadth, nose widths, IPD, bitragion breadth, and more), and 3D scan capture. Comfort is assessed via 5-point Likert scales every 30 minutes across 2–4 hour sessions. Slippage is tracked via a proctor-observed protocol. FOV, eye tracking registration, and multi-location comfort data are collected in parallel — findings feed directly into industrial design, mechanical engineering, and product decisions.
Every analysis starts from two physical inputs, both captured in our lab. The device's CAD model is 3D-scanned on its own, and the participant is scanned wearing the device during a fitting session. The CAD geometry represents design intent; the participant scan represents physical reality on a real human head. (The frame shown here is a non-proprietary example used purely to demonstrate methodology.)
CAD reference views
The device CAD is aligned to the participant's fit scan — taken with the device worn — and compared against the participant's baseline head scan (captured separately wearing a wig cap). This reveals exactly how the device seats on that individual's facial geometry, and whether it falls within the intended design envelope. Below: one participant with a successful fit, one with a population mismatch.
Fit scan + baseline head scan — natural seating, frame within envelope
Fit scan + baseline head scan — frame elevated, sits outside contact zones
Contact maps quantify skin-to-frame contact at every surface point, color-coded by proximity. Blue = no contact (frame held away from the head). Green = normal, light contact. Red = high-pressure contact, where the frame presses into the head. Together with the in-person session data, these maps let me investigate exactly where and why a fit succeeds or fails.
Good fit views
Poor fit views
Scan analysis verifies and deepens the comfort findings gathered during the in-person fitting session — confirming where contact concentrations align with the discomfort participants reported, with precise anatomical location data for the engineering team.
By showing exactly how the temple arms and nose pieces contact the head — the interfaces that actually hold the device in place — contact analysis helps explain the slippage behavior observed in person, grounding session findings in measurable geometry rather than proctor estimation alone.
Using the eye-box CAD linked to the frame, I calculate center of eye rotation (CER), pupil position, and eye-box placement from digitized anthropometric landmarks — the geometry that governs where virtual content lands in the visual field, how sharply it resolves, and the binocular alignment that drives visual comfort and reduces eye strain. Can be more accurate than in-session proctor evaluation.
Cross-sectional cuts at key anatomical landmarks reveal internal clearance geometry not visible from surface maps. Temple arm opening angles and pitch delta are measured across participants and compared against the population database — data the mechanical engineering team uses directly for clamp force and structural fit decisions.
Methods & Toolkit
UX Research
Human Factors
Domains & Tools
Contact
Open to conversations about UX Research, Human Factors, and Spatial Computing. Based in South Florida.