Peiling is a Ph.D. Candidate in the Cognitive Science Research Group, Department of Computer Science, Queen Mary University of London. Her research interests lie in building novel deep transfer learning and fair machine learning algorithms for context-based adolescent cyberbullying detection models. She holds a master's degree in software engineer and has worked as a software engineer and project manager for several global software companies
During the enrichment period, Peiling will mainly focus on improving the generality of context-aware cyberbullying detection systems and investigating biases in these current models. The approach to building these models will be directly linked to the Multi-Agent Systems, Robust Machine Learning, Natural Language Processing and Machine Learning, and Dynamic Systems Interest Groups, which face the challenge of "making algorithmic systems fair, transparent, and ethical." Their research will explore how to make models behave fairly and how to explain their debiasing behavior, which is also the focus of the Fairness, Transparency, and Privacy interest group.