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Bailu Zhang

Title: Teaching Fellow

Department: Computer Science and Engineering

College: College of Engineering

Curriculum Vitae

Curriculum Vitae Link

Current Scheduled Teaching

No current or future courses scheduled.

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 1015.004Computing Tools and Techniques LaboratorySpring 2025 SPOT
CSCE 1015.006Computing Tools and Techniques LaboratorySpring 2025 SPOT
CSCE 1015.012Computing Tools and Techniques LaboratorySpring 2025 SPOT
MATH 1680.505Elementary Probability and StatisticsSpring 2019 Syllabus SPOT
MATH 1680.160Elementary Probability and StatisticsFall 2018 Syllabus SPOT
MATH 1680.161Elementary Probability and StatisticsFall 2018 Syllabus
MATH 1680.162Elementary Probability and StatisticsFall 2018 Syllabus
MATH 1680.163Elementary Probability and StatisticsFall 2018 Syllabus
MATH 1680.330Elementary Probability and StatisticsSpring 2018 Syllabus SPOT
MATH 1680.331Elementary Probability and StatisticsSpring 2018 Syllabus SPOT
MATH 1680.332Elementary Probability and StatisticsSpring 2018 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

  • Zhang, B., Feng, S., Parajuli, M., Xiong, Y., Pan, C., Guo, X. (2024). SEMQuant: Extending Sipros-Ensemble with Match-Between-Runs for Comprehensive Quantitative Metaproteomics. International Symposium on Bioinformatics Research and Applications. 102--115.

    • Journal Article

  • Feng, S., Ji, H., Wang, H., Zhang, B., Sterzenbach, R., Pan, C., Guo, X. (2022). MetaLP: An integrative linear programming method for protein inference in metaproteomics. PLOS Computational Biology. 18 (10) e1010603. Public Library of Science San Francisco, CA USA.
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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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