From Psychology to Artificial Intelligence: Yijing (Tabitha) Han’s Path in Product Design - The Ritz Herald

From financial-data platforms to cross-cultural communication tools, Yijing Han’s work spans enterprise software, artificial intelligence, and tangible interaction. Across these areas, she has maintained a consistent interest in the relationship between people and technology.

Her starting point was not design but psychology. She majored in psychology and minored in digital media as an undergraduate before pursuing a Master of Communication in Digital Media at the University of Washington. That study of cognition, behavior, and decision-making became the foundation for how she approaches complex products — and her first encounters with user-centered design turned a curiosity about people into a decision to design for them. Understanding people, she realized, was only the starting point; turning that understanding into something useful was the work she wanted to do.

From Understanding Behavior to Defining Problems

Faced with a complex system, she begins by considering how users interpret information, what informs their decisions, and which parts of the existing process create unnecessary cognitive effort. Research and interviews, in her view, are not used to support a predetermined solution but to confirm that the problem has been understood correctly — a matter of method rather than intuition.

She summarizes her working method in five statements: why before what; clarity from ambiguity; simple from complex; bring people with me; and AI you can trust.

The relevance of that method to enterprise financial technology is fairly direct. Financial products contain dense information and complex relationships, and users may need to reach a judgment under time constraints; a designer must consider not only whether an interface is usable, but also how information is prioritized, how the system responds, and whether users understand the consequences of their actions. AI products add a further layer: output is uncertain, yet users may act on it. The design problem therefore extends to how people interpret a result, when they should intervene, and how an error is corrected.

Working With Complex Financial Systems at PitchBook

Han is a Product Designer at PitchBook Data, a Morningstar company, based in Seattle, with more than six years in enterprise financial technology. She is the sole designer on PitchBook Mobile and on the enterprise entitlements and role-based access control (RBAC) system, and she led the design of PitchBook’s first AI-driven data collection tool as well as the design of Market Maps. The projects sit at different points in the production, organization, access, and use of financial data.

On mobile, Han’s most consequential decision came from questioning an assumption baked into the product. Research revealed that the primary mobile users were not associate-level analysts, as the desktop platform assumed, but senior leaders across the private equity and investment industry — people who needed to grasp a situation and surface the right insight in minutes, not operate a full analytical toolset. Rather than compress the desktop experience onto a smaller screen, she redefined the mobile product around that audience: prioritizing at-a-glance oversight and fast access to the insights these decision-makers actually reach for. The redesign significantly reduced the time users needed to surface relevant insights and contributed to substantial growth in daily engagement; the app currently holds a 4.9 rating on the App Store.

AI-assisted data collection and access management sit closer to the operating foundations of enterprise software. On the data tool, Han redivided the work: machines handle extraction and repetitive processing, while people still judge sources and check quality. The design questions followed from that split — what status the system shows, where a person has to confirm, and how machine output can be corrected. She calls this “AI you can trust”: trust comes from transparency and control, not from users accepting the system’s conclusion. Since the tool shipped, it has helped significantly expand the platform’s data-collection capacity and driven increased user engagement with the workflow. The entitlements project turned rules about roles, resources, and exceptions into workflows administrators could follow, substantially reducing administrative workload and cutting access-related support requests.

Sotto: Turning to Cross-Cultural Communication

Outside her enterprise work, Han has explored the relationship between people and AI through independent projects. Sotto — Cultural Communication Companion is a multimodal AI companion for non-native English speakers in the workplace. A linguistically accurate sentence can land differently across organizations or cultures; Sotto asks whether AI can also account for the situation in which communication occurs, helping users interpret another person’s intent and respond in a way suited to the setting. Han summarizes the idea as “read the room, not the language,” and describes the system’s role as providing support rather than making the judgment for the user.

She also designed Home Signal Tray, an independent project in ambient computing that works through physical interaction, and contributed to SOMA, an AI wearable system for autistic and neurodivergent children, for which she was responsible for the companion app’s information architecture, navigation, design system, and gamification features. These projects sit at the opposite end of the spectrum from her enterprise work — one a set of high-stakes financial data systems, the other consumer products addressed to an individual’s circumstances.