UX and Human Factors Researcher
UX, usability & human factors research for AI wearables and AR/XR, from user studies to CAD & scan analysis
About
I am a UX and Human Factors Researcher at Magic Leap, currently on assignment at Google, conducting human-centered research across rapid prototyping and hardware development for AR/XR and AI-enabled wearable devices. At its core, my work is user research: usability studies, user interviews, and evaluations of UI, interaction, and AI-feature experiences that ground product decisions in real user behavior rather than assumption. My recent work supports Android XR, delivered through Magic Leap’s Platform Services engagement as a partner to Google, alongside Magic Leap reference prototypes.
My work bridges behavioral UX research and physical human factors. I contribute across study design, user interviews, data analysis, and synthesis as part of the research team, while owning scan analysis from capture through reporting. Across 30+ end-to-end studies spanning usability, diary, field, and comparative research, plus 2,000+ human subjects and 600+ scan alignments, 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 & Human Factors Researcher II · On assignment at Google · Apr 2025 – Present · Plantation, FL
UX & Human Factors Researcher I · Magic Leap · Mar 2023 – Mar 2025 · Plantation, FL
Mars Research
Market Research Intern · Jan 2023 – Mar 2023 · Fort Lauderdale, FL
Florida Atlantic University
BBA Business Administration, Management
Published Research
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, with 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 supporting Android XR, delivered through Magic Leap as a partner to Google, alongside Magic Leap reference prototypes. Spans 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 comparing AR/XR interaction modalities and competitor hardware, spanning hand tracking vs. controller performance (SUS, perceived comfort, accuracy scales), hand anthropometry, and arm/wrist fatigue assessment. Competitive evaluation and focus groups across industry-leading headsets and smart glasses, assessing real-world capability: live language translation, text reading, visual identification of objects and surroundings, navigation while walking and cycling, and teleprompter use.
User Research · AI Wearables
AI Feature Experiences
Research into how people use and experience AI features on wearable devices: multimodal, camera-based AI that translates speech and text; identifies objects, plants, artwork, and music; answers questions about whatever the user is looking at; and surfaces contextual actions like visual search. The work examines when these experiences feel genuinely useful, where they break down, and why. These are the usability and trust questions that decide whether AI on a wearable actually helps.
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.
Field Research · Android XR
Wayfinding & Navigation
A two-week outdoor field study on walking navigation with AR glasses, run in peak South Florida summer conditions. The research looked at how people consult wayfinding guidance while on the move, and how navigation content should behave relative to where someone is actually looking. I also captured FLIR thermal readings on the device in the field, evaluating how its temperature held up under real outdoor heat. Delivered through Magic Leap as a partner to Google.
Concept Testing · Magic Leap
Spatial UI: Home Menu
Comparative concept testing of redesigned home menu layouts and their spatial behaviors: how a menu should follow, dock, or stay anchored as a user turns their head and moves through a space. Evaluated with internal participants across stationary, mobile, and dual-task scenarios to compare preference and usability between concepts.
Case Studies
Benchmarking · Mixed-Method
Can people navigate an AR operating system with their hands alone, well enough to ship it on by default? A 28-participant mixed-method benchmark across the full task suite, and the rating-scale instrument behind it.
The Challenge
Direct-interaction hand tracking was a candidate to enable by default, but it was unknown whether first-time users could complete core tasks (device setup, scrolling, text entry, sliders, browsing) without frustration or fatigue.
My Contribution
I contributed across protocol and rating-scale design, moderation, behavioral coding, and synthesis as part of the research team.
Methods
28 external, native users across a within-subjects task suite. Mixed-method: SUS, structured observation, and per-task rating scales for discoverability, ease of use, perceived accuracy, and perceived comfort.
Method
Moderated, in-person · Within-subjects
Participants
28 external, native users
Instruments
SUS · Rating scales · Observation
Study Instrument: Per-Task Rating Scales
Discoverability
Ease of Use
Perceived Accuracy
Perceived Comfort
Outcome
The benchmark drove a conditional-ship recommendation and a prioritized redesign brief covering the depth-feedback cursor, virtual keyboard, and gesture instruction, delivered to product and engineering.
Moderated Usability · Iterative
How do first-time users learn to operate an AR device across hand, controller, and voice input? Iterative usability research that directly shaped Fundamentals, a Magic Leap onboarding app launched to teach the basics of AR.
The Challenge
Introducing someone to AR means teaching an entirely new interaction paradigm, with no established mental model to fall back on, across users with very different expectations, learning styles, and tolerance for frustration.
My Contribution
Contributed across study rounds, spanning moderation, behavioral coding, and synthesis, as part of the research team.
Methods
Multiple rounds of moderated, think-aloud usability testing with first-time AR users, progressing from controlled lab sessions to real-world deployment environments such as private offices, open factory floors, conference rooms, and homes, to see how the experience held up where users would actually encounter it.
From the Study: Direct-Interaction (Poke) Onboarding
My hand interacting with the Fundamentals onboarding app, poking virtual objects to learn direct interaction.
Outcome
Findings shaped each iteration of the app’s content, flow, and interaction design, including tutorial priorities and in-context guidance. This was the research behind the shipped onboarding experience.
Comparative Evaluation
When virtual content meets the edge of the display, how should it disappear? A within-subjects study of three rendering conditions across real AR tasks, delivered as end-user and developer guidance.
The Challenge
The point where rendered content clips at the display boundary affects perceived solidity, depth, and visual comfort, but the “right” behavior wasn’t defined.
My Contribution
Helped design the study and contributed across moderation, persona coding, and synthesis.
Methods
37 external participants evaluated three conditions, a hard-clip baseline, a conservative near-fade, and a liberal no-clip, across spatial object search, direct UI interaction, and close-range reading. Preference ratings, persona coding to segment participants, and a structured qualitative debrief.
The Display Envelope: Where Clipping Happens
Field of view by viewing distance, from Magic Leap’s published Human Interface Guidelines. Clipping occurs where rendered content meets the edge of this frustum, the boundary this study probed.
Outcome
Findings revealed nuanced preferences around content solidity, perceived movement, and perceived visual comfort, and were formatted as end-user and developer guidance published to the AR developer portal.
Remote Diary Study · Galaxy XR
What does it feel like to live with a headset over a month, not a session? A month-long remote diary study supporting Galaxy XR, with an unmoderated diary phase bookended by 1:1 interviews, delivered through Magic Leap’s Platform Services engagement as a partner to Google.
The Challenge
Some questions only answer themselves over time, in how comfort, attention, and adoption evolve once a device becomes part of someone’s daily routine. A diary study is the right tool to capture that lived experience, alongside the moderated lab research it complements.
My Contribution
Helped design and run the study, from activity design and survey instrumentation to data cleaning, longitudinal synthesis, and stakeholder reporting.
Methods
A month-long study: an unmoderated diary phase, with participants self-reporting through structured prompts and surveys in their own environments, bookended by 1:1 user interviews at the start and end, tracking perceived cognitive comfort, mental load, social acceptability, and perceived physical comfort over time.
Outcome
The longitudinal, in-context format gave a richer view of how people actually live with the device, showing how adoption and comfort shift with real-world use over time, complementing moderated lab research and feeding product and design decisions.
Research Craft
Personas, participant segmentation, journey maps, and day-in-the-life studies are how I turn raw research into something product and design teams can act on. They translate messy behavior into shared reference points that guide prioritization, flows, and trade-offs.
The artifacts below are illustrative, built to show the format and depth of this work. The real deliverables are under NDA.
Participant Segmentation
Segment 01
First-Time Explorer
New to AR, with no mental model to fall back on. Needs guidance, reassurance, and early wins to build confidence.
Segment 02
All-Day Wearer
Wears the device across a full shift. Comfort, fit, and cognitive load decide whether the product survives past lunch.
Segment 03
Power Creator
Pushes advanced features and multitasks. Wants speed, precision, and control, and abandons anything that slows them down.
Persona Deep-Dive
Marcus, 41
The All-Day Wearer
Field Technician
Tech Comfort
Tolerance for Friction
Context
Warehouses and outdoor sites, variable lighting, gloves on, hands full.
Goals
Behaviors
Frustrations
What Would Win Trust
“If it hurts by lunch, I stop wearing it, no matter how smart it is.”
Journey Map: Emotional Arc
Unbox & Set Up
Doing
Opens the device, follows first-run setup.
Feeling
Curious, a little cautious.
Friction
Unsure what to do first.
First Use
Doing
Tries core gestures and controls.
Feeling
Excited but tentative.
Friction
Inputs do not always register.
Learning the Basics
Doing
Works through tutorials and prompts.
Feeling
Frustrated, second-guessing.
Friction
Too much to learn at once.
Daily Wear
Doing
Uses it for real tasks on the job.
Feeling
Confident, capable.
Friction
Comfort over long sessions.
Making It Routine
Doing
Reaches for it out of habit.
Feeling
Trusting, reliant.
Friction
Wants deeper, faster features.
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 an anthropometry database spanning thousands of participants: traditional 1D measurements, facial landmarking, and sub-millimeter-accurate 3D head scans across an exceptionally broad range of ages, ethnicities, and facial geometries, the statistical foundation for fit validation at population scale.
In-person sessions combine anatomical landmarking by palpation (glabella, tragion, nasion, infraorbitale, zygion), traditional anthropometric measurements (head breadth, face breadth, nose widths, IPD, bitragion breadth), and 3D scan capture. Comfort is rated on 5-point Likert scales every 30 minutes across 2–4 hour sessions, slippage via a proctor-observed protocol, with FOV and eye-tracking registration captured in parallel, with findings feeding 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.