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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 5934.863Directed StudyFall 2024
CSCE 5210.003Fundamentals of Artificial IntelligenceFall 2024
CSCE 6940.866Individual ResearchFall 2024
CSCE 6940.863Individual ResearchSummer 10W 2024
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 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

  • Forhad, A., Shi, W. Enhancing Annotation Quality through Active Re-labeling Strategies in Deep Active Learning. International Jounral on Artificial Intelligence Tools (IJAIT). International Joint Conferences on Artificial Intelligence(Name of the conference).
  • Rafiq, R., Shi, W., Albert, M.V. Wearable Sensor-Based Few-Shot Continual Learning on Hand Gestures for Motor-Impaired Individuals via Latent Embedding Exploitation. International Jounral on Artificial Intelligence Tools (IJAIT). International Joint Conferences on Artificial Intelligence (Name of the conference).
  • Yu, D., Shi, W., Qi, Y. Discover-Then-Rank Unlabeled Support Vectors in the Dual Space for Multi-Class Active Learning. Machine Learning: Science and Technology. International Conference on Machine Learning (name of the conference, did not shown in drop down menu).
  • Shi, W., Yu, D., Yu, Q. Actively Testing Your Model While It Learns: Realizing Label-Efficient Learning in Practice. International Conference on Neural Information Processing. Conference on Neural Information Processing Systems(Conference name, does not shown in drop downs.).
  • Zhu, Y., Shi, W., Bao, W., Yu, Q. Prompt is All You Need: Towards Open World Learning. International Conference on Computer Vision (ICCV). IEEE.
  • Shi, W., Yu, D., Shi, Q. STARS: Spatial-Temporal Active Re-Sampling for Label-Efficient Learning from Noisy Annotations. AI Magazine. United States AAAI Press.

    • Journal Article

  • Shi, W., MOSES, H., Yu, Q., MALACHOWSKY, S., KRUTZ, D. ALL: Supporting Experiential Accessibility Education and Inclusive Software Development. IEEE Transactions on Software Engineering. Association for Computing Machinery.
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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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