Weishi Shi

Weishi Shi

About Me

I am a Ph.D student at Machine Learning and Data-intensive Computing Lab at Rochester Institute of Technology, mentored by Professor Qi Yu.

My research focuses on active learning, a special human-in-the-loop machine learning principle that leverages human experts’ knowledge for efficient and economic training. Active learning is useful especially for knowledge-rich domains such as bioinformatics, military and medical. In those domains, we often find that the labeled data are limited, and annotating the unlabeled data requires domain experts who are difficult to find and expensive to hire.

Publications

  • “A Gaussian Process-Bayesian Bernoulli Mixture Model for Multi-Label Active Learning”. Shi, Weishi, et.al. Neural Information Processing Systems (NIPS) 2021
  • “Active Learning with Maximum Margin Sparse Gaussian Processes”. Shi, Weishi, \& Yu, Qi. International Conference on Artificial Intelligence and Statistics (AISTATS) 2021
  • “Multifaceted Uncertainty Estimation for Label-Efficient Deep Learning”. Shi, Weishi, et.al. Neural Information Processing Systems (NIPS) 2020
  • “A Bayesian learning model for design-phase service mashup popularity prediction”. Moayad Alshangiti, Shi, Weishi, et.al. Expert Systems with Applications (ESWA) 2020
  • “Presenting and Evaluating the Impact of Experiential Learning in Computing Accessibility Education”. Shi, Weishi, et.al. International Conference on Software Engineering (ICSE) 2020
  • “Integrating Generative and Discriminative Sparse Kernel Machines for Multi-class Active Learning”. Shi, Weishi, \& Yu, Qi. Neural Information Processing Systems (NIPS) 2019
  • “Fast Direct Search in an Optimally Compressed Target Space for Efficient Multi-Label Active Learning”. Shi, Weishi, \& Yu, Qi. International Conference on Machine Learning (ICML) 2019.
  • “Integrating Multi-level Tag Recommendation with External Knowledge Bases for Automatic Question Answering”. Lima, E., Shi, Weishi., Liu, Xumin., \& Yu, Qi. ACM Transactions on Internet Technology (TOIT) 2019.
  • “An Efficient Many-Class Active Learning Framework for Knowledge-Rich Domains.” Shi, Weishi, \& Yu, Qi. IEEE International Conference on Data Mining (ICDM) 2018.
  • “From Novice to Expert Narratives of Dermatological Disease”. Obot, N., O’Malley, L., Nwogu, I., Yu, Q., Shi, Weishi., \& Guo, X. 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.

Academic Services

  • Year 2021
    • The Association for the Advancement of Artificial Intelligence:(AAAI):PC member
    • Neural Information Processing Systems(Neural-IPS):PC member
    • The International Conference on Machine Learning(ICML):PC member
    • International Conference on Computer Vision(ICCV):subreviewer
    • British Machine Vision Conference(BMVC):subreviewer
  • Year 2020
    • The Association for the Advancement of Artificial Intelligence:(AAAI):PC member
    • Neural Information Processing Systems(Neural-IPS):PC member
    • The International Conference on Machine Learning(ICML):PC member
    • The Medical Image Computing and Computer Assisted Intervention(MICCAI):subreviewer
    • IEEE Transactions on Knowledge and Data Engineering(TKDE):subreviewer
    • IEEE International Conference on Web Services (ICWS):subreviewer
    • IEEE International Conference on Services Computing(SCC):subreviewer
    • International Conference on Artificial Intelligence and Statistics(AISTATS):subreviewer
  • Year 2019
    • IEEE International Conference on Cloud Computing(CLOUD):subreviewer
    • SMCA:subreviewer

*Year 2018

  • IEEE Transactions on Services Computing(TSCSI): subreviewer.
  • Knowledge and Information Systems(KAIS): subreviewer.
  • IEEE International Conference on Cloud Computing(CLOUD):subreviewer
  • IEEE International Conference on Big Data:subreviewer
  • International Symposium on Social Networks Analysis, Management and Security:subreviewer

  • Advanced Distribution Management Systems(ADMS):subreviewer

  • Year 2017
    • Big Data and Applications (BDAP): subreviewer
    • International Conference on Information Reuse & Integration for Data Science(IRI):subreviewer
    • International Conference on Service Oriented Computing(ICSOC):subreviewer
    • IEEE Transactions on Services Computing(TSC):subreviewer
    • International Conference on Advanced Data Mining and Applications(ADMA):subreviewer
    • IEEE International Conference on Web Services (ICWS): Student volunteer

Teaching Experience

  • Knowledge Process Technologies (ISTE 612-01)
    • Graduate course, Data mining basics. In person.
    • 2020, Lecturer
    • 2017-2020 Teaching assistant
  • Knowledge Process Technologies (ISTE 612-01)
    • Graduate course, Information retrieval and machine learning basics. Online
    • 2019,Lecturer
  • Analytical Thinking (ISTE 600-01)
    • Graduate course, Data mining basics, In person
    • 2017,Lecturer
    • 2018-2020, Teaching assistant
  • Data-Driven Knowledge Discovery (ISTE 780-01)
    • Graduate course, Machine mining basics, In person
    • 2017-2020, Teaching assistant CV ====== See my CV here