Hello! My name is Jiaming Qu (瞿佳明) and I am now an applied scientist at Amazon. I recently got my Ph.D. degree in Information Science at School of Information and Library Science (SILS), UNC Chapel Hill in May 2025, where I was advised by Prof. Yue Wang and Prof. Jaime Arguello. Prior to my Ph.D., I got my Master's degree in Information Science at SILS.
My research lies at the intersection of Explainable Artificial Intelligence (XAI), Human-Computer Interaction (HCI) and Information Retrieval (IR) with two primary goals. I develop algorithms, visualizations, and interactive tools to help users understand AI systems. I also conduct empirical studies to investigate how users interact with these tools. My research can be grouped into three key themes chronologically:
- Explainable ranking algorithm in IR: I developed a ranking algorithm that explains the relevance of search results without compromising performance for biomedical literature search (SIGIR'20 and ECIR'21) and an ontology-based search system for neuroscience.
- Human-XAI Interaction: I developed interactive XAI tools to help users understand machine learning predictions. I conducted a quantitative study to evaluate their effects in assisting human decision-making (CIKM'21) and a qualitative study to investigate users' cognitive activities when interacting with such tools (CHIIR'23).
- Explaining Unintuitive Explanations: I developed LLM-based interactive tools to explain predictive but unintuitive words in text analysis (e.g., "problems" is predictive of positive Amazon reviews, or "Chicago" is predictive of deceptive hotel reviews). I conducted mixed-method studies to investigate how users interact with these tools (FAccT'24 and FAccT'25).