Portfolio

Role

UX Research Intern

Organization

Early-stage AI startup

Timeline

Summer 2026

Methods

Secondary research; in-depth interviews with older adults and caregivers

The challenge

The startup had a rough prototype of a virtual-assistant chatbot and was moving toward an MVP. The product rested on one core assumption: caregivers are the customer. Older adults were framed as a burden on their caregivers, and caregivers would pay for a product that eased it.

Investors were interested but wanted more proof of concept. The team brought me in to work backwards and gather evidence for that assumption.

My approach

• Started with what's already known. Before any interviews, I reviewed existing research on aging, caregiving, and safeguarding. This gave the team established best practices for designing for and researching with older adults, and let me focus our interviews on open questions instead of re-learning what the literature already shows.
• Talked to both sides of the relationship. I interviewed caregivers and older adults rather than only the presumed buyer, so the research could test the assumption instead of just confirming it.
• Chose methods that suit this population. I advised against surveys and short interviews, which tend to work poorly with older adults, and argued for longer, more relational approaches.
• Treated participants as a vulnerable population. I reminded the team throughout that older adults need extra care in recruitment, consent, and how sessions are run.

What I found

• Older adults want independence. They didn't see themselves as a problem to be managed, and they care about their autonomy as much as anyone.
• Caregivers see support as love, not a burden. The "burden" framing at the heart of the pitch didn't match how caregivers described helping their family members.
• Switching costs are high, especially for older adults:
  • Many don't want to text with a chatbot.
  • They want to escalate quickly to a real person.
  • They want dynamic, back-and-forth conversation.
  • Asking others for help is often a valued social connection, not friction that needs removing.

Impact

The findings challenged the team's early assumptions about who the customer is and what problem the product solves. By the end of my internship, the team had begun reframing the product's direction and its investor pitch around independence rather than burden.

What I learned

This internship taught me how to push back on methods and on founding assumptions respectfully, as a partner to the team rather than a blocker, while keeping user wellbeing and needs at the center. When research goes against what a team hopes to hear, how you deliver it matters as much as the findings.

Role

Lead researcher & first author

Organization

Northwestern University

Timeline

Interviews Jun–Aug 2025; presented at DIS Jun 2026

Methods

15 semi-structured interviews; reflexive thematic analysis

The challenge

Research and popular media increasingly link dating apps to burnout, anxiety, and poorer well-being, yet user retention stays steady. That's a contradiction worth understanding: if dating apps feel so depleting, why do people keep using them? We asked how users' feelings shape their decisions to stay, and how they manage those feelings over time.

My approach

• Interviewed 15 active dating app users aged 18–44, each for 55–75 minutes, and recruited until no new themes emerged.
• Let participants name their own feelings and strategies, with prompts like "How do you generally feel after using dating apps?" and "What made you come back?"
• Analyzed the interviews with reflexive thematic analysis, with three researchers coding independently in Dedoose. We used a feelings wheel to give the team a shared language for emotions without restricting what counted as one.

What I found

• Staying runs on a cycle of emotional ups and downs. Recurring frustration, anxiety, and fatigue push people toward leaving, but brief moments of validation and, above all, hope pull them back in.
• People build their own ways to keep going: emotionally detaching from bad outcomes, limiting how much they use the apps (for example by pausing their profile), and leaning on friends for support.
• Those coping strategies make the apps bearable, which keeps people engaged even when the experience stays hard.

Impact

The research points to product opportunities that could reduce burnout while keeping users engaged in a healthier way:

• Help users focus on fewer, more promising connections.
• Add emotional check-in tools so users can notice how the app makes them feel over time.
• Better support intentional breaks and smoother returns to the app.

It also flags where product changes alone won't help, so teams can set realistic goals. The work was published and presented at DIS 2026.

What I learned

Retention numbers alone can hide a poor experience: users may stay because they're hopeful, not because they're satisfied. This project taught me to look beneath engagement metrics at how people actually feel, and to be clear with stakeholders about which problems a product can solve and which it can't.

Role

Lead analyst & first author

Organization

Northwestern University

Timeline

Published Nov 2025

Methods

Secondary analysis of 25 interviews; reflexive thematic analysis

The challenge

Research suggests that sharing in ways that feel authentic can benefit well-being, so platforms are trying to design for it. BeReal takes the most rigid approach: it prompts everyone at a random moment and discourages late, retaken, or edited posts. But does a single, product-defined version of "authentic" match how people experience it?

My approach

• Treated BeReal as a bounded case study, because its design makes authenticity an explicit product goal.
• Reanalyzed 25 existing interviews with active BeReal users aged 18–24 (BeReal's largest demographic). My research group had collected them for a larger project on self-presentation, and I took a new angle: the tensions that shape how people practice authenticity.
• Analyzed the interviews with reflexive thematic analysis, with two researchers coding independently in Dedoose before building shared themes.

What I found

• Users experience two facets of authenticity: posting impromptu, in the moment, and representing themselves honestly. The two often conflict, and BeReal's design only supports the first.
• Authenticity competes with other values. Users wanted privacy (the prompt might arrive at work or in class), autonomy over what they share, and the ability to reminisce about meaningful moments.
• Users work around the constraints: posting late, changing the scene, or curating anyway, even though they know it goes against the app's intent.

Impact

The research turned into a practical framework teams can use to evaluate features: how much control users have over when content is captured, what is shared, and how it's edited. It also led to concrete product recommendations. For example, BeReal could send several prompts a day and let users pick one, keeping spontaneity while giving users back some choice. The paper received a Best Paper Honorable Mention at CSCW 2025.

What I learned

When users work around a feature, that's a signal the product's assumptions don't fit their lives. This project taught me to treat workarounds as research data, and to translate a fuzzy value like "authenticity" into specific design choices a team can act on.

Role

Sole researcher (MA thesis)

Organization

Columbia University

Timeline

2022–2023

Methods

App walkthrough; survey (n=75); 10 semi-structured interviews

The challenge

To use a dating app, people have to share personal details with a large, unknown audience. The usual assumption is that users are in full control of what they share, so people who share a lot must not care about privacy. I wanted to test that assumption by asking: how do Hinge's design and its users' social norms shape what people disclose on their profiles?

My approach

• App walkthrough. I analyzed Hinge's marketing, privacy policy, and onboarding flows screen by screen to see how the product itself encourages disclosure.
• Survey of 75 Hinge users (45 multiple-choice and open-ended questions), including a scenario question about how they judge a "sparse" profile.
• 10 follow-up semi-structured interviews with survey respondents, analyzed together with the open-ended survey answers to identify the social norms around what to share.

What I found

• The design nudges people to share more. The profile settings mix fields that are shown by default with fields that are hidden by default, so users have to pay close attention to stay in control of what appears on their profile.
• Users know the risks. All 75 respondents named at least one privacy risk, most often unintended people seeing their profile, followed by stalking and harassment.
• Users share anyway, because of social norms. "Sparse" profiles were judged as "low effort," so users felt pressure to share enough, while carefully choosing what to leave out to avoid being judged. On average, respondents shared 9 of the 20 optional profile details.

Impact

The research identified specific points in the onboarding flow where design pushes users to share more than they might choose to, and points to concrete fixes, such as consistent and clearly labeled privacy defaults, that would help users make deliberate choices and build trust in the product.

What I learned

Pairing a product audit with user research gives a much fuller picture: the walkthrough showed what the product encourages, and the survey and interviews showed why users go along with it. I now look for ways to combine methods so findings cover both the product and its users.

Role

Inaugural Graduate Research Fellow

Organization

The Privacy Lab at Columbia University

Timeline

Aug 2022 – Aug 2023

Methods

Digital ethnography across 3 online communities; open coding

The challenge

Dating apps require people to share identifying details (name, age, location, face) with a large audience of strangers, and nothing stops other users from screenshotting a profile and posting it elsewhere. I kept seeing everyday users do exactly that, often with conflicting views and double standards. My research question: why are dating app profiles posted to Reddit, TikTok, and Facebook?

My approach

• Picked three communities with different norms so I could compare the same behavior across platforms: the Facebook group "Are We Dating the Same Guy NYC," the r/Tinder subreddit, and the #Hinge hashtag on TikTok.
• Ran a digital ethnography, taking field notes on the language, patterns, and etiquette of posts in each community.
• Used open coding to build a set of post types and motivations directly from the data, rather than starting from fixed categories.

What I found

• On Facebook, sharing works as a safety network. Most posts asked for "tea" or "red flags" on a man or warned others about him. Posters stayed anonymous while the men's faces and details were on full display, which is a privacy double standard, but one that the safety purpose depends on.
• On Reddit, people gave up their own privacy to get help. Men mostly posted their own Tinder profiles asking for feedback, often sounding burned out and hopeless, while community rules had them blur other people's details.
• On TikTok, sharing was mostly entertainment: viral trends that roast other people's profiles and voice prompts without their consent, alongside women sharing their own profiles to give advice.
• Across all three, privacy was traded against other needs, such as safety, support, or entertainment. Looking only at privacy oversimplifies the problem: shutting these spaces down would also remove real benefits, like the safety network.

Impact

The findings surface unmet user needs that dating platforms could address directly: safety and vetting (users are doing this work themselves in outside communities) and profile feedback. They also caution against simply shutting these communities down, since users rely on them. I published the findings on Medium, and the work was later cited in Wired.

What I learned

The same behavior can serve very different needs depending on the platform. Comparing communities side by side taught me to look past a single lens like privacy and ask what users are actually getting out of a behavior, which is where the product opportunities are.