MIXED-METHODS
Studies
Interviewed
Degree
Fernando Luna
I listen closely, study carefully, and turn what I hear into evidence teams can act on — close to the people the field often skips past: pregnant women, manufacturing operators, faculty and academic staff, people managing chronic illness.
Core Expertise
Featured
POTS Access Technology
Co-designed with a primary user living with POTS; grounded in medical literature and triangulated against community insights from POTS, dysautonomia, and chronic-illness forums.
The user experiences sudden, debilitating POTS episodes — rapid heart rate spikes, dizziness, brain fog, and risk of fainting — triggered by standing or positional changes. Existing tools did not provide real-time detection, guided intervention, or comprehensive symptom tracking. The user needed a way to detect episodes early, receive immediate guided support, log symptoms for medical appointments, and carry emergency features — all without drawing unwanted attention to their condition.
Created a comprehensive, research-driven solution addressing both physical and digital needs of a POTS patient
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More Projects
TruePulse Health — ANS Monitoring for Maternal Care
Maternal-care ANS monitoring platform with Polar H10 sensor
Darzy.ai — Founding Product Research in Sustainable Fashion
Lead Product Researcher at a pre-product sustainable-fashion startup
Energy Insights Network (ESN)
Energy monitoring dashboard for manufacturing
Canvas AI-Assisted Quiz Creation
AI-assisted quiz creation research for Canvas faculty
Research
Qualitative-led mixed methods. I lead with depth — interviews, observation, synthesis — and bring quantitative methods in to validate, not to lead.
Qualitative Depth
- •Semi-structured user interviews
- •Contextual inquiry & field research
- •Think-aloud usability testing
- •Heuristic evaluation
- •Affinity mapping & thematic coding
- •Stakeholder & expert interviews
Quantitative Validation
- •Time-on-task & click-count analysis
- •System Usability Scale (SUS)
- •Confidence intervals
- •Task completion rates
- •Measurement planning
AI-Augmented Research
- •NotebookLM for qualitative synthesis
- •Gemini for theoretical-framework application
- •Confidence-band reliability framework
- •AI trust & explainability validation
- •Agentic AI feature testing
Operations & Ethics
- •IRB-aware research protocols
- •IU research lab coordination
- •Cross-role participant recruiting
- •Multi-stakeholder facilitation
- •GDPR-aware data handling
Case
A closer look at how studies came together — the questions, the methods, and what we learned.
Spotify Un(Wrapped)
Music discovery & social listening UX research
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Research Assistant — IU Indianapolis
HCI research across two labs
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Rethinking Identity & Anonymity on Threads
Social media identity research
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UX Research Process
How I move from a fuzzy question to something a team can act on — three phases, designed to fit how research actually shows up in product work.
Plan the Question
Good research starts with the right question, not the right method. I work with the team to frame what we're trying to learn, agree on scope and what success looks like, and pick methods that fit — qualitative for the why, quantitative for the how much.
Gather Evidence
Mixed-methods studies designed to fit the question — interviews, observational task analysis, think-aloud testing, heuristic evaluation, and field research with people who don't use products the way designers do. Recruitment is intentional. Ethics are upstream of the protocol, not after.
Synthesize & Share
Raw data isn't insight. I cluster observations through affinity mapping, code findings thematically, and translate patterns into recommendations the team can move on. Then I share the findings the way they'll actually land — workshops, readouts, and stories instead of static decks.
What it's all for
A researcher's portfolio should reveal how they think, not just what they shipped.
Every phase here is built to make the next product decision a little better than the last one — methods chosen to fit the question, findings translated into actions a team can defend, impact measured against a real baseline.