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Weishi Shi

Title: Assistant Professor

Department: Computer Science and Engineering

College: College of Engineering

Curriculum Vitae

Curriculum Vitae Link

Education

  • PhD, Rochester Institute of Technology, 2022
    Major: Computing and Information Sciences
  • MS, Rochester Institute of Technology, 2016
    Major: Information Sciences and Technologies

Current Scheduled Teaching

CSCE 6280.001Advanced Topics in Artificial IntelligenceSpring 2026
CSCE 5934.863Directed StudySpring 2026
CSCE 6950.963Doctoral DissertationSpring 2026
CSCE 6940.963Individual ResearchSpring 2026
CSCE 4290.002Introduction to Natural Language ProcessingSpring 2026 Syllabus
CSCE 5290.002Natural Language ProcessingSpring 2026
Texas Education Code 51.974 (HB 2504) requires each institution of higher education to make available to the public, a syllabus for undergraduate lecture courses offered for credit by the institution.

Previous Scheduled Teaching

CSCE 5934.863Directed StudyFall 2025
CSCE 6950.863Doctoral DissertationFall 2025
CSCE 6940.866Individual ResearchFall 2025
CSCE 5215.002Machine LearningFall 2025 SPOT
CSCE 2900.763Special Problems in Computer Science and EngineeringFall 2025
CSCE 6950.963Doctoral DissertationSummer 10W 2025
CSCE 5934.863Directed StudySpring 2025
CSCE 6950.963Doctoral DissertationSpring 2025
CSCE 6940.963Individual ResearchSpring 2025
CSCE 5215.004Machine LearningSpring 2025 SPOT
CSCE 5934.863Directed StudyFall 2024
CSCE 5210.003Fundamentals of Artificial IntelligenceFall 2024 SPOT
CSCE 6940.866Individual ResearchFall 2024
CSCE 6940.863Individual ResearchSummer 10W 2024
CSCE 5934.863Directed StudySpring 2024
CSCE 6940.963Individual ResearchSpring 2024
CSCE 5215.004Machine LearningSpring 2024 SPOT
CSCE 5210.003Fundamentals of Artificial IntelligenceFall 2023 SPOT
CSCE 6940.866Individual ResearchFall 2023
CSCE 6940.966Individual ResearchSpring 2023
CSCE 5215.004Machine LearningSpring 2023 Syllabus SPOT
CSCE 5210.003Fundamentals of Artificial IntelligenceFall 2022 Syllabus SPOT
Texas Education Code 51.974 (HB 2504) requires each institution of higher education to make available to the public, a syllabus for undergraduate lecture courses offered for credit by the institution.

Published Intellectual Contributions

    Conference Proceeding

  • Li, M., Shi, W., Yu, D., Yu, Q. (2024). Evidential Mixture Machines: Deciphering Multi-Label Correlations for Active Learning Sensitivity. Advances in Neural Information Processing Systems 37. 112217-112236. Neural Information Processing Systems Foundation, Inc. (NeurIPS). https://doi.org/10.52202/079017-3563
  • Al Forhad, M.A., Shi, W. (2024). Balancing Explanations and Adaptation in Offline Continual Learning Systems Using Active Augmented Reply. 2024 IEEE 7th International Conference on Multimedia Information Processing and Retrieval (MIPR). 24 484-490. IEEE. https://doi.org/10.1109/mipr62202.2024.00082
  • Li, M., Qiu, J., Shi, W. (2024). Macro-AUC-Driven Active Learning Strategy for Multi-Label Classification Enhancement. 2024 IEEE 7th International Conference on Multimedia Information Processing and Retrieval (MIPR). 18 280-286. IEEE. https://doi.org/10.1109/mipr62202.2024.00052
  • Abu-Shaira, M., Shi, W. (2024). Unveiling Statistical Significance of Online Regression Over Multiple Datasets. 2024 IEEE 7th International Conference on Multimedia Information Processing and Retrieval (MIPR). 274-279. IEEE. https://doi.org/10.1109/mipr62202.2024.00051
  • Alshangiti, M., Shi, W., Lima, E., Liu, X., Yu, Q. (2022). Hierarchical Bayesian multi-kernel learning for integrated classification and summarization of app reviews. Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 558-569. ACM. https://doi.org/10.1145/3540250.3549174
  • Zhu, Y., Shi, W., Pandey, D.S., Liu, Y., Que, X., Krutz, D.E., Yu, Q. (2021). Uncertainty-Aware Multiple Instance Learning from Large-Scale Long Time Series Data. 2021 IEEE International Conference on Big Data (Big Data). 1772-1778. IEEE. https://doi.org/10.1109/bigdata52589.2021.9671469
  • Shi, W., Khan, S., El-Glaly, Y., Malachowsky, S., Yu, Q., Krutz, D.E. (2020). Experiential learning in computing accessibility education. Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Companion Proceedings. 250-251. ACM. https://doi.org/10.1145/3377812.3390901
  • El-Glaly, Y., Shi, W., Malachowsky, S., Yu, Q., Krutz, D.E. (2020). Presenting and evaluating the impact of experiential learning in computing accessibility education. Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Software Engineering Education and Training. 49-60. ACM. https://doi.org/10.1145/3377814.3381710
  • Shi, W., Yu, Q. (2018). An Efficient Many-Class Active Learning Framework for Knowledge-Rich Domains. 2018 IEEE International Conference on Data Mining (ICDM). 1230-1235. IEEE. https://doi.org/10.1109/icdm.2018.00164
  • Obot, N., OrMalley, L., Nwogu, I., Yu, Q., Shi, W., Guo, X. (2018). From Novice to Expert Narratives of Dermatological Disease. 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). 131-136. IEEE. https://doi.org/10.1109/percomw.2018.8480162
  • Shi, W., Liu, X., Yu, Q. (2017). Correlation-Aware Multi-Label Active Learning for Web Service Tag Recommendation. 2017 IEEE International Conference on Web Services (ICWS). 229-236. IEEE. https://doi.org/10.1109/icws.2017.37
  • Shi, W., Liu, X., Yu, Q. (2017). Correlation-Aware Multi-Label Active Learning for Web Service Tag Recommendation. 2017 IEEE International Conference on Web Services (ICWS). 229-236. IEEE. https://doi.org/10.1109/icws.2017.37
  • Shi, W., Liu, X., Yu, Q. (2017). Correlation-Aware Multi-Label Active Learning for Web Service Tag Recommendation. 2017 IEEE International Conference on Web Services (ICWS). 229-236. IEEE. https://doi.org/10.1109/icws.2017.37

    • Journal Article

  • Abu Shaira, M., Feng, Y., Fan, H., Shi, W. (2026). OLC-WA: Drift aware tuning-free online classification with weighted average. Expert Systems with Applications. 306 130848. Elsevier BV. https://doi.org/10.1016/j.eswa.2025.130848
  • Shaira, M.A., Feng, Y., Fan, H., Shi, W. (2025). OLC-WA: Drift Aware Tuning-Free Online Classification with Weighted Average. Expert Systems with Applications. 130848. Elsevier.
  • Abu-Shaira, M., Shi, W. (2025). OLR-WAA: Adaptive and Drift-Resilient Online Regression with Dynamic Weighted Averaging. Other. Springer Science and Business Media LLC. https://doi.org/10.1007/s41019-025-00312-y
  • Shi, W., Moses, H., Yu, Q., Malachowsky, S., Krutz, D.E. (2024). ALL: Supporting Experiential Accessibility Education and Inclusive Software Development. Other. 33 (2) 1-30. Association for Computing Machinery (ACM). https://doi.org/10.1145/3625292
  • Alshangiti, M., Shi, W., Liu, X., Yu, Q. (2020). A Bayesian learning model for design-phase service mashup popularity prediction. Expert Systems with Applications. 149 113231. Elsevier BV. https://doi.org/10.1016/j.eswa.2020.113231
  • Lima, E., Shi, W., Liu, X., Yu, Q. (2019). Integrating Multi-level Tag Recommendation with External Knowledge Bases for Automatic Question Answering. Other. 19 (3) 1-22. Association for Computing Machinery (ACM). https://doi.org/10.1145/3319528
  • Liu, X., Shi, W., Kale, A., Ding, C., Yu, Q. (2017). Statistical Learning of Domain-Specific Quality-of-Service Features from User Reviews. Other. 17 (2) 1-24. Association for Computing Machinery (ACM). https://doi.org/10.1145/3053381
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Overall
Summative Rating		 Challenge and
Engagement Index		 Response Rate

out of 5
out of 7		 %
of
students responded
  • Overall Summative Rating (median):
    This rating represents the combined responses of students to the four global summative items and is presented to provide an overall index of the class’s quality. Overall summative statements include the following (response options include a Likert scale ranging from 5 = Excellent, 3 = Good, and 1= Very poor):
    • The course as a whole was
    • The course content was
    • The instructor’s contribution to the course was
    • The instructor’s effectiveness in teaching the subject matter was
  • Challenge and Engagement Index:
    This rating combines student responses to several SPOT items relating to how academically challenging students found the course to be and how engaged they were. Challenge and Engagement Index items include the following (response options include a Likert scale ranging from 7 = Much higher, 4 = Average, and 1 = Much lower):
    • Do you expect your grade in this course to be
    • The intellectual challenge presented was
    • The amount of effort you put into this course was
    • The amount of effort to succeed in this course was
    • Your involvement in course (doing assignments, attending classes, etc.) was
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