About Me

I am a 5th year PhD student in the Department of Computer Science at Emory Unvieristy advised by Dr. Emily Wall in the CAV Lab.

My reserach interests include:

  • Developing visual interfaces to promote personal awareness (e.g., biased behaviors) in data analysis and decision making.
  • Developing viusal interfaces to promote awareness in personal data analysis (e.g., time tracking data).
Yanan Da

Research Projects

Visual Salience to Mitigate Gender Bias in Letters of Recommendation

We conducted a crowdsourced experiment that showed biased language can have a negative impact on readers’ evaluations of candidates and visual highlighting of specific types of language in letters of recommendation has the potential to influence the evaluation of candidates. Specifically, we found that highlighting language more commonly used to describe female can negatively affect readers’ evaluation of candidates, while highlighting language more commonly used to describe male can reduce the effects of the bias.
Paper       Supplemental

VA‑supported Bias‑aware Univeristy Admissions

We designed and implemented a visualization system for university admissions that promotes reviewers’ self-reflection and scrutiny to ensure fair and consistent review processes. The visualization interface logs reviewers’ interactions in order to provide a granular analysis of review behaviors across attributes such as race and gender of applicants which can relate to potentially biased processes and inform review procedures in subsequent cycles. We conducted a crowdsourced study and a case study where the system was used for Ph.D. admissions in our department to evaluate the effectiveness of the system.

Real-time Contact Tracing and Risk Monitoring via Privacy-Enhanced Mobile Tracking

We expanded a contact tracing Mobile App for COVID19 with privacy enhancement on user locations using Geo-Indistinguishability mechanism and provides risk monitoring. The system allows users to control and refine the precision with which their information will be collected and used, and enable: 1) contact tracing of individuals who are exposed to infected cases and identification of hot-spot locations, 2) individual risk monitoring based on the locations they visit and their contact with others.
Paper       Source code


Visual Salience to Mitigate Gender Bias in LORs
Y. Da, M. Chen, B. Altschuler, Y. Bu, and E. Wall
Workshop on Visualization for Social Good (VIS4Good, at VIS'23), 2023.

React: Real-time contact tracing and risk monitoring via privacy-enhanced mobile tracking
Y. Da, R. Ahuja, L. Xiong, and C. Shahabi
IEEE International Conference on Data Engineering, 2021.