معرفی
Qi Li is an Assistant Professor at Iowa State University, specializing in Artificial Intelligence, Machine Learning, and Data Science. Their research focuses on advancing techniques in natural language processing, graph theory, federated learning, and knowledge graph construction. Key contributions include improving distant supervision for named entity recognition, enhancing fairness in graph-based systems, and developing adaptive methods for knowledge extraction and crowdsourcing.
Research interests span a broad range of topics, including but not limited to: Named Entity Recognition (NER), fairness in machine learning, federated learning frameworks, knowledge graph completion, and optimization of crowdsourcing systems. Their work often combines theoretical advancements with practical applications, such as improving model robustness and scalability in real-world scenarios.
Recent publications highlight innovations in entity linking re-ranking (GenDecider), personalized federated learning (pFedClub), and individual fairness in graph structures. These contributions reflect a commitment to addressing challenges in AI ethics, data efficiency, and system reliability.
Qi Li’s research has been presented at top-tier conferences such as COLING, WWW, NeurIPS, and AIES, demonstrating a strong track record in impactful and interdisciplinary work.
