I am a Ph.D. student in the School of Computer Science at Carnegie Mellon University, advised by Nihar Shah. Previously, I received a B.S. in EECS from UC Berkeley, where I worked with Laura Waller on computational imaging.

My research interest is in machine learning, particularly in applications to improving the process of peer review and crowdsourcing. In these applications, the goal of my research is to understand and mitigate various biases using tools from computer science and statistics, and to also have real-world impact through outreach and policies.


Email: jingyanw [at] cs.cmu.edu


Research
The goal of my research is to understand and mitigate various sources of biases in decision making problems, such as in peer review and peer grading. To understand the biases, I use tools from statistics and computer science to identify their causes and analyze their extent. To mitigate the biases, I work on outreach and policies that lead to practical impacts. Specifically, my research considers three major components that are potentially biased: people, algorithms and policies.
Publications

Teaching