Conducting human factors research on wide range of Apple’s products.

• Recruit study participants, create research protocols, run user study sessions, conduct human factors tasks, analyze study results, communicate with cross functional partners.

• Design and conduct generative and evaluative research across hardware and software experiences, particularly leveraging AI technologies.

Assess and shape AI system behaviors through human-centered evaluation frameworks.

• Collaborate with design, engineering, and our own research team to prototype and iterate on AI-driven interfaces and emerging interaction paradigms.

• Led nationwide qualitative user studies across 50+ commercial and 50+ residential sites, training 10+ moderators on hardware/software, Python scripts, and protocol compliance, ensuring study-compliant insights, validated data accuracy, and improving participant engagement and retention by 15% through empathetic onboarding and clear communication.

• Delivered a data set of 1500+ verified hours of field research, directly influencing the client’s AI model training and product strategy decisions.

• Diagnosed and resolved hardware, software, and Python script issues during data collection, ensuring uninterrupted workflows, minimal downtime, and compliance with strict project quality standards.

• Coded 50+ interview data to understand what people think about water conservation in Arizona. Collaborating as a UI designer/QA for interactive games, testing generative AI chatbot called Blu.

• Contributing toward examining the heuristics of the Arizona Department of Environmental Quality, ADEQ website to help boost website engagement metric by 60%. I applied user-centered design processes and methodologies such as interviews, 70+ surveys, 30 usability tests, data analysis, persona, storytelling, and four user flows.

• I work primarily as a UX researcher for the water conservation museum exhibit in Arizona. A part of my job is to analyze interview data and code them to understand what people say about water conservation in the state of AZ. I also work as a UI designer by designing interactive widgets, freebies, and an AI chatbot called Blu!

Created CRUX Lab's social media content and designed 15+ posts to drive engagement and followers.

I collaborated on an eye-tracking research project on public park signages and wayfinding with my manager, partnering with Project Cities - City of Clarkdale and other volunteers. We created a research plan, recruited participants, and scheduled a moderated eye-tracking usability test with 20 participants.

Analyzed quantitative data in MS Excel and Tobii Pro Lab to understand eye fixations and saccades.

Co-authored a publication on "Tracking visual patterns and interaction with public park signage using eye-tracking technology."

Equipment used: Tobii Glasses 3 for gaze data, Tobii Pro Screen reader for testing digital park signage.

• Analyzed Tonal Inc.’s home gym equipment with software such as iMotions and Tobii Pro Lab. Measured EDA (Electrodermal Activity) with the Empatica E4 watch and eye-tracking gaze data with Tobii Glasses 3.

• Conducted a usability test with 24 participants covering 100% of Tonal’s UI, evaluated usability test data, and suggested improvements in a report of physical and UI interaction to enhance Tonal’s learning and navigational experience by 30%.

Completed the CITI program's

IRB - Biomedical research mandatory training certification.

RCR - Responsible Conduct of Research certification.

Worked at Adidas - ASU a corporate partner to

• Research equipment used: Tobii Glasses 3 and Empatica E4 watch.

Used Tobii Glasses for eye tracking and analyzing how gaze and pupil dilation trends are associated with emotions and decision-making. Empatica E4, a medical-grade watch was used to analyze EDA (Electrodermal activity) that measures skin electrical conductance changes.

• Research software used:

Worked on home gym equipment by Tonal Inc. with software such as iMotions and Tobii Pro Lab.

Used MS Excel to analyze the exported data by creating pivot tables.

• Process: Recruited 24 participants with specific demographics provided by Tonal.

Participants were given 7 tasks that covered high-priority testing pages in Tonal’s UI and physical arms. Task success rates, errors, and time were recorded.

Conducted a usability test with at least 24 participants, analyzed the testing data, and suggested improvements in the product's physical and UI interaction.

Collected data by observation and think-a-loud exercise to confirm the research findings.

Finally, participants were given a post-test questionnaire to understand their overall experience, best task, feature, unpleasant experiences, and suggestions. Used a Likert scale from 1-5.

Mapped a valence scale that showed participants positive and negative experiences from -5 being the worst to +5 being the best.