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Julie Vera

Julie A. Vera, Ph.D.

Weatherfluencers, trust, and collective sensemaking · UX/product research leader

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I study why people trust unofficial interpreters when it matters most, and I build the research functions that help product teams ask the same kind of question.

In my academic life, I study how audiences evaluate YouTube-native weather creators whose professional standing is contested. My research examines how credibility, legitimacy, usefulness, and trustworthiness are worked out in real time through radar interpretation, official warnings, source comparison, local reports, livestream chat, and visible revision.

In industry, I've built 0→1 research functions at AutoNation and Monster, standing up research infrastructure, method, and organizational vocabulary in places that had none. Across 12+ years in UX research, analytics, and social computing, my peer-reviewed work spans CHI, CSCW, HAI, and ISCRAM.

Current research

I'm extending my dissertation work beyond single livestreams to study why audiences return to particular weatherfluencers across severe weather events, when trust accumulates or erodes, and how creators become familiar, useful, or suspect over time.

I'm especially interested in the community layer around severe weather interpretation: livestream chat, local observation, ground-truth reports, personal weather stations, volunteer networks, moderation, vouching, and other forms of participation that help audiences make unfolding conditions actionable.

I'm not treating weather as a metaphor for everything. The weather case matters because it makes a difficult problem unusually observable: how people evaluate interpretation while expertise, evidence, accountability, and risk are all still in motion.

Research in practice

My industry work focuses on evidence for high-consideration decisions: how people interpret information, where confidence breaks down, and what product teams need to know before they commit to a direction.

At AutoNation, I lead research across vehicle discovery, selling and trade-in experiences, online purchase flows, content strategy, photography standards, AI-assisted shopping tools, and customer confidence in automotive retail.

Measurement for ambiguous human judgments
Defining what teams need to learn, what evidence should count, and how to evaluate constructs like trust, confidence, usefulness, reliance, and decision quality when there is no single clean answer.
Trust signals in high-consideration decisions
Studying how shoppers and sellers interpret offers, next steps, data requests, vehicle details, dealership expectations, and cues that either reassure them or make them hesitate.
Search, discovery, and content interpretation
Understanding how customers use search, filters, vehicle detail pages, educational content, terminology, and AI-assisted explanations to make sense of complex options.
Visual evidence and merchandising credibility
Studying how photography, editing, image consistency, backgrounds, distortions, and vehicle condition cues shape perceived transparency and willingness to continue shopping.

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Selected publications & talks

View publications and talks →

Contact

For research leadership roles, collaborations, talks, writing, or strange high-stakes information problems, get in touch.