Topic

Dayi Lin

Affiliation

Huawei Canada, Kingston, ON, Canada

Topic

Code Review,Deep Learning Models,Game Development,Software Engineering,Training Deep Learning Models,User Reviews,Video Games,Accuracy Of Model,Accuracy Of Regression Model,Average Root Mean Square,Average Square,Best Practices,Bias Training,Bootstrapping Technique,Causal Impact,Classification Performance,Classification Task,Classifier Training,Contextual Factors,Control Issues,Correlation-based Approach,Coverage Metrics,Data Augmentation,Deep Learning,Deep Learning Framework,Deep Learning Training,Deep Neural Network,Deep Neural Network Model,Defect Prediction,Dependent Variable,Detection Approach,Distribution Of Metrics,Dynamic Library,Early Stopping,Evaluation Criteria,Film Criticism,Foundation Model,Game Consoles,Game Genres,Game Users,Gaming Platforms,Generation Of Mutants,Global Model,Hallucinations,Hardware Architecture,Heuristic Approach,High Accuracy Results,ImageNet,Important Metrics,Individual Projects,

Biography

Dayi Lin received the Ph.D. degree in computer science from the Software Analysis and Intelligence Laboratory, Queen’s University, Canada. He is currently a Staff Researcher with the Centre for Software Excellence, Huawei Canada, working on the topics of SE for AI and FMware (i.e., FM-powered software). His research interests include SE for AI, AI for SE, game engineering, and mining software repositories. His work has been published at several top-tier software engineering venues, such as TSE, TOSEM, EMSE, and ICSE. His work on SE and games has also attracted wide media coverage, including kotaku, PC gamers, gamasutra, and national newspapers. He is the Program Co-Chair of The 1st International Conference on AIware (co-located with FSE’24).