Hi there, I'm Yiwen!
I'm a PhD candidate in cognitive psychology,
working with Dr. Ben Rottman at
Univeristy of Pittsburgh, and a UX researcher intern at Google in 2024 and at
Meta in 2022.
Before that, I received my B.S in Psychology from Chu Kochen Honors College, Zhejiang University.
My research interest centers around human causal learning and decision making.
I'm also passionate about using both quantitative and qualitative methods to explore human behavior.
Outside the lab, I enjoy bouldering, cooking, crocheting, and hanging out with my dog!
I'm actively looking for UX Researcher roles in Q4 2024!
My Role: UX Researcher
Timeline: 12 weeks
This project aimed to answer questions such as "which features have low awareness compared to other features within a team or across teams, and how should we prioritize them for improvement?" I used survey methodology to collect data over 6000 users across 5 countries on 60 WhatsApp features and quantitative analysis to provide research insights for feature prioritization based on users' awareness. The methods involve feature comparisons across teams, user groups, yearly data and competitor apps.
Solutions: Survey Design | Large-scale Data Analysis| User Segments Analysis | Competitor Comparison
View Details
My Role: UX Researcher
Timeline: 3 month
The main goal of the larger project is designing intuitive interface panels for Advanced Driver Assistance Systems (ADAS) for SAIC Motor (stakeholder) and providing insights towards ADAS industry regulation in China. I led an qualitative project to explore drivers’ experience with ADAS on the competitor models such as Tesla Model S, Volvo XC60, or Geely Borui.
Solutions: In-depth Interview | Contextual Inquiry | Competitor Analysis | Survey
View DetailsMy Role: Principle Investigator
Timeline: 1 year
As technology (e.g. health-tracking app or apple watch) enables individuals to monitor their health data (e.g. weights, mood) through apps and wearables, the crucial question arises: How effectively can people interpret these observations for informed lifestyle decisions? This project systematically investigates human reasoning with various interrupted time series scenarios and different presentation formats (e.g. graphs vs instances; PC vs smartphone). Offered actionable design recommendations to augment data interpretation and inference-making capabilities in everyday life
Solutions: A/B testing | Large-scale Data Analysis | Cluster Analysis | Diary Study
My Role: Researcher, Designer and Full-stack Developer
Timeline: 3 month
Most of my experiments are conducted outside the lab scenerio. I developed a research platform on Google Cloud, enabling remote data collection
from 2000+ participants across 5+ experiments and significantly expanding research reach during COVID-19.
To enhance user experience and increase participant retention rates, I conducted multiple A/B tests and usability tests,
effectively managing large-scale data collection with less than 1% attrition.
I also built a psychCloud template based on
Flask, Vuejs and Firestore to share with the psychology community.
Solutions: A/B testing | Usability Test | Prototype Feedback
My Role: Data Analyst and Data Engineer
Timeline: 1 week
I created an interactive Shiny app to explore the Youtube Trending Data since August 2020, with 65391 records. This data dashboard enables intuitive visualization using R shiny and ggplot2. The dashboard showcases dynamic features like filters, scatterplots, bar plots, and histograms, offering a comprehensive view of the dataset's trends.
View the interactive dashboard!
Zhang, Y. & Rottman, B. M. (2023). Casual Learning with Delays Up to 21 Hours. Psychonomic Bulletin & Review
Zhang, Y. & Rottman, B. M. (2023). Causal Learning with Interrupted Time Series Data. Judgment and Decision Making
Cowan, E. T., Zhang, Y., Rottman, B., & Murty, V. P. (under review). The effects of mnemonic variability and spacing on memory over multiple timescales. Preprint
Zhang, Y. & Rottman, B. M. (2021). Casual Learning with Delays Up to 21 Hours. Proceedings of the 43rd annual conference of the cognitive science society. PDF | Poster
Zhang, Y. & Rottman, B. M. (2021). Casual Learning with Interrupted Time Series. Proceedings of the 43rd annual conference of the cognitive science society. PDF | Poster
Zhang, Y., Yang, Z., Liang, J., Wu, F., Gao, Z. (2018, July). Object-based Attention, not Spatial Attention, is Critical for Encoding Feature Binding in Visual Working Memory. 14th Asia-Pacific Conference on Vision and the 3rd China Vision Science Conference.