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Russel L. Pears

Title: Clinical Associate Professor

Department: Computer Science and Engineering

College: College of Engineering

Curriculum Vitae

Curriculum Vitae Link

Education

  • PhD, University of Wales, 1992
    Major: Computer Science
    Dissertation: Data Allocation for Response Time Minimisation in Parallel Database Systems

Current Scheduled Teaching

CSCE 5210.001Fundamentals of Artificial IntelligenceSpring 2025
CSCE 4201.001Introduction to Artificial IntelligenceSpring 2025
CSCE 5215.001Machine LearningSpring 2025
CSCE 5215.005Machine LearningSpring 2025
CSCE 5215.600Machine LearningSpring 2025
CSCE 5934.824Directed StudyFall 2024
CSCE 5210.001Fundamentals of Artificial IntelligenceFall 2024
CSCE 5210.600Fundamentals of Artificial IntelligenceFall 2024
CSCE 5215.005Machine LearningFall 2024
CSCE 5950.824Master's ThesisFall 2024
CSCE 5214.001Software Development for Artificial IntelligenceFall 2024

Previous Scheduled Teaching

CSCE 5380.400Data MiningSummer 10W 2024 SPOT
CSCE 5950.824Master's ThesisSummer 10W 2024
CSCE 5210.001Fundamentals of Artificial IntelligenceSpring 2024 SPOT
CSCE 5210.002Fundamentals of Artificial IntelligenceSpring 2024 SPOT
CSCE 4201.001Introduction to Artificial IntelligenceSpring 2024 Syllabus SPOT
CSCE 5215.002Machine LearningSpring 2024 SPOT
CSCE 5215.600Machine LearningSpring 2024 SPOT
CSCE 5950.824Master's ThesisSpring 2024
CSCE 5210.001Fundamentals of Artificial IntelligenceFall 2023 SPOT
CSCE 5210.002Fundamentals of Artificial IntelligenceFall 2023 SPOT
CSCE 5210.600Fundamentals of Artificial IntelligenceFall 2023 SPOT
CSCE 5950.824Master's ThesisFall 2023
CSCE 5214.002Software Development for Artificial IntelligenceFall 2023 SPOT
CSCE 4380.002Data MiningSummer 10W 2023 Syllabus SPOT
CSCE 5380.001Data MiningSummer 10W 2023 SPOT
CSCE 5380.002Data MiningSummer 10W 2023 SPOT
CSCE 5210.001Fundamentals of Artificial IntelligenceSpring 2023 SPOT
CSCE 5210.002Fundamentals of Artificial IntelligenceSpring 2023 Syllabus SPOT
CSCE 5210.600Fundamentals of Artificial IntelligenceSpring 2023 SPOT
CSCE 4201.001Introduction to Artificial IntelligenceSpring 2023 Syllabus SPOT
CSCE 5215.002Machine LearningSpring 2023 SPOT
CSCE 5215.003Machine LearningSpring 2023 Syllabus SPOT
CSCE 5215.600Machine LearningSpring 2023 SPOT
CSCE 5900.824Special ProblemsSpring 2023
CSCE 5210.001Fundamentals of Artificial IntelligenceFall 2022 Syllabus SPOT
CSCE 5210.002Fundamentals of Artificial IntelligenceFall 2022 Syllabus SPOT
CSCE 5210.600Fundamentals of Artificial IntelligenceFall 2022 Syllabus SPOT
CSCE 5214.002Software Development for Artificial IntelligenceFall 2022 Syllabus SPOT
CSCE 5900.824Special ProblemsFall 2022 Syllabus
CSCE 5380.001Data MiningSummer 10W 2022 SPOT
CSCE 5380.002Data MiningSummer 10W 2022 SPOT
CSCE 5210.001Fundamentals of Artificial IntelligenceSpring 2022 Syllabus SPOT
CSCE 4201.001Introduction to Artificial IntelligenceSpring 2022 Syllabus SPOT
CSCE 5215.002Machine LearningSpring 2022 Syllabus SPOT
CSCE 5215.003Machine LearningSpring 2022 Syllabus SPOT
CSCE 5215.600Machine LearningSpring 2022 Syllabus SPOT
CSCE 5214.001Software Development for Artificial IntelligenceSpring 2022 Syllabus SPOT
CSCE 5150.001Analysis of Computer AlgorithmsFall 2021 SPOT
CSCE 5150.003Analysis of Computer AlgorithmsFall 2021 SPOT
CSCE 5210.001Fundamentals of Artificial IntelligenceFall 2021 SPOT
CSCE 5210.008Fundamentals of Artificial IntelligenceFall 2021
CSCE 5214.002Software Development for Artificial IntelligenceFall 2021 SPOT

Published Intellectual Contributions

    Conference Proceeding

  • Pears, R.L. (2022). Utilizing Noise as an Attack Independent Measure for Representing Privacy in Logistic Cumulative Noise Addition.
  • Pears, R. (2021). Fingerprinting Concepts in Data Streams with Supervised and Unsupervised Meta-Information. Yes, this was part of Ben Halstead's PhD research whom I supervised when I was in New Zealand. 2021 IEEE 37th International Conference on Data Engineering (ICDE). https://ieeexplore.ieee.org/abstract/document/9458895
  • Journal Article

  • Pears, R. (2022). Novel method for optimizing performance in resource constrained distributed data streams. Yes, this is a paper that is part of Rashi Bhalla PhD research. Rashi is a former PhD student of mine.. 1-19. New York, Springer. https://doi.org/10.1007/s10489-021-03019-5
  • Pears, R. (2021). Analyzing and repairing concept drift adaptation in data stream classification. Yes, part of Ben Halstead's PhD research which I supervised along with members of staff at the University of Auckland, New Zealand. 1-35. Springer. https://link.springer.com/article/10.1007/s10994-021-05993-w
  • Pears, R. (2021). Recurring concept memory management in data streams: exploiting data stream concept evolution to improve performance and transparency. 35 796-836. SpringerLink. https://link.springer.com/article/10.1007/s10618-021-00736-w
  • Pears, R. (2021). Topical affinity in short text microblogs. Yes, this was part of Herman Wandabwa's work whom I supervised when I was at the Auckland University of Technology in New Zealand. Wiley.
  • Pears, R.L. Multi-interest semantic changes over time in short-text microblogs. 228 (27) Elsevier. https://doi.org/10.1016/j.knosys.2021.107249

Contracts, Grants and Sponsored Research

    Contract

  • Pears, R.L., "Generating Predicate Calculus Rules from Cyber Security Recommendations specified in English," sponsored by University of North Texas, University of North Texas, $7500 Funded. (2023 - 2023).
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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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