Jingyan Wang

Research Abstract:

High-stakes decision-making problems – in the form of estimating the quality of items or people – arise in many real-world applications such as admissions, peer review, grading, and hiring. My research focuses on understanding and improving these decision-making systems using technical tools from computer science and statistics. I draw inspiration from the psychology literature to model real-world phenomena, develop algorithms with provable theoretical guarantees, and implement policy changes that have made practical impact. My research studies three interconnected components: (1) I develop algorithms that optimize application-specific objectives. These algorithms improve fairness in learning from pairwise comparisons (e.g., peer grading and preference learning) and reliability in recruitment (e.g., admissions and hiring) without sacrificing accuracy or efficiency. (2) I identify different sources of human bias and propose intuitive models that are supported by psychology literature. These models are general without making restrictive parametric assumptions such as linearity. Under these models, I develop bias correction methods, prove theoretical guarantees on these methods, and demonstrate that these methods outperform naive methods that neglect such subtleties in human behavior. (3) I design the procedures of evaluation processes. I identify tradeoffs incurred by different allocation schemes that distribute the evaluation task to reviewers, and design new data elicitation interfaces that combine ratings and rankings to achieve the best of both worlds. The techniques that I have developed are general, and have wider implications in theoretical problems in statistical machine learning and high-dimensional estimation. My research is interdisciplinary and has been published in top venues in machine learning, artificial intelligence, human computation, economics and computation, and statistics.

Bio:

Jingyan Wang is a President's postdoctoral fellow in the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology. She received her Ph.D. from the School of Computer Science at Carnegie Mellon University, advised by Nihar Shah, and her B.S. in Electrical Engineering and Computer Sciences with a minor in Mathematics from the University of California, Berkeley. She uses tools from statistics and machine learning to understand and improve high-stakes decision-making systems such as those involving hiring and admissions. Her research is interdisciplinary and has been published in top venues in statistics, machine learning, artificial intelligence, human computation, and economics and computation. She is the recipient of the Best Student Paper Award at AAMAS 2019, and was selected as a Rising Star in EECS and in Data Science.