Rate My Professor Yanchao Yu

Dr. Yanchao Yu is a Lecturer in Computer Science in the School of Computing, Engineering and the Built Environment at Edinburgh Napier University. He obtained his BSc in Computer Science and Technology from Beijing Information Science and Technology University between 2007 and 2011. He then pursued an MSc in Software Engineering at Heriot-Watt University from 2012 to 2013, followed by a PhD in Artificial Intelligence from the same institution between 2014 and 2018. His doctoral thesis, titled 'Optimising strategies for learning visually grounded word meanings through interaction,' was completed in 2018. Additionally, he holds a PGCert in Higher Education from Advance HE, awarded between 2021 and 2022. Prior to his current role, Yu worked as a Research Associate at Heriot-Watt University, contributing to projects in natural language processing and AI.

Yu's research specializations encompass conversational AI, multimodal interaction, machine learning, and robotics. He is an active member of the Centre for Artificial Intelligence and Robotics (CAIR) at Edinburgh Napier University and serves as the Principal Investigator for the EPSRC-funded Learning from Collaborative Storytelling (LoCS) project, where he develops and integrates multimodal knowledge extraction techniques and AI-driven storytelling frameworks. In teaching, he delivers the module 'Machine Learning for Conversational AI.' His scholarly output includes over 30 publications, with key works such as 'MoDEsT: a Modular Dialogue Experiments and Demonstration Toolkit' (2023, ACM), 'Coronabot: A Conversational AI System for Tackling Misinformation' (2021), 'Combining Visual and Social Dialogue for Human-Robot Interaction' (2021), 'Interactive Learning through Dialogue for Multimodal Language Grounding' (2015), and contributions to conferences like LREC and AAAI Symposium. His research has accumulated 278 citations on Google Scholar. Yu also supervises PhD students in AI-related topics, including mitigating LLM-generated disinformation via machine unlearning and multimodal LLMs in ER diagram evaluation.