WeishiShiAssistant ProfessorComputer Science and EngineeringArtificial Intelligence and Data EngineeringFull-Time FacultyWeishi.Shi@unt.eduDiscovery Park F227

Office Hours:
Wed 3:00 - 4:00 pm for CSCE 5415
Fri 2:00 - 3:00 pm for CSCE 6280Full-Time FacultyComputer Science and EngineeringArtificial Intelligence and Data EngineeringFaculty Info | Research Profile | Google Scholar | Accordion Heading Content Education PhD in Computer Science, Rochester Institute of Technology, 2022 MS in Information Science & Technology, Rochester Institute of Technology, 2016 BS in Software Engineering, Xi'an Jiao Tong University, 2009 Research My research interest lies in the general fields of machine learning and knowledge discovery, with a specific focus on active learning and its applications in diverse knowledge domains. My long-term research vision is to develop the next-generation machine learning paradigm, where the machine learns along with humans rather than blindly from humans. My research has been mainly published in machine learning and data mining venues, including NeurIPS, ICML, AISTATS, and ICDM. Publications Shi, Weishi, et.al. ”A Gaussian Process-Bayesian Bernoulli Mixture Model for Multi-Label Active Learning”. Neural Information Processing Systems (NIPS) 2021 Shi, Weishi, & Yu, Qi. ”Active Learning with Maximum Margin Sparse Gaussian Processes”. International Conference on Artificial Intelligence and Statistics (AISTATS) 2021 Shi, Weishi, et.al. ”Multifaceted Uncertainty Estimation for Label-Efficient Deep Learning”. Neural Information Processing Systems (NIPS) 2020 Moayad Alshangiti, Shi, Weishi, et.al. ”A Bayesian learning model for design-phase service mashup popularity prediction” Expert Systems with Applications (ESWA) 2020 Shi, Weishi, et.al. ”Presenting and Evaluating the Impact of Experiential Learning in Computing Accessibility Education” International Conference on Software Engineering (ICSE) 2020 Shi, Weishi, & Yu, Qi. ”Integrating Generative and Discriminative Sparse Kernel Machines for Multi-class Active Learning”. Neural Information Processing Systems (NIPS) 2019 Shi, Weishi, & Yu, Qi. ”Fast Direct Search in an Optimally Compressed Target Space for Efficient Multi-Label Active Learning”. International Conference on Machine Learning (ICML) 2019. Lima, E., Shi, Weishi., Liu, Xumin., & Yu, Qi. ”Integrating Multi-level Tag Recommendation with External Knowledge Bases for Automatic Question Answering”. ACM Transactions on Internet Technology (TOIT) 2019. Shi, Weishi, & Yu, Qi. ”An Efficient Many-Class Active Learning Framework for Knowledge-Rich Domains.” IEEE International Conference on Data Mining (ICDM) 2018. Obot, N., O’Malley, L., Nwogu, I., Yu, Q., Shi, Weishi., & Guo, X. ”From Novice to Expert Narratives of Dermatological Disease”. IEEE International Conference on Pervasive Computing and Communications Workshops 2018. Shi, Weishi., Liu, X., & Yu, Q. ”Correlation-Aware Multi-Label Active Learning for Web Service Tag Recommendation.” In 2017 IEEE International Conference on Web Services (ICWS) 2017. Liu, X., Shi, Weishi., Kale, A., Ding, C., & Yu, Q. ”Statistical Learning of Domain-Specific Quality-of-Service Features from User Reviews.” ACM Transactions on Internet Technology (TOIT) 2017.