Weishi Shi

Computer Science and Engineering
Assistant Professor

Discovery Park F227

Weishi Shi
Areas of Expertise:
  • Artificial Intelligence and Data Engineering
About

Faculty Info | Research Profile | Google Scholar |

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.