Faculty Profile

Russel Pears

Title
Clinical Associate Professor
Department
Computer Science and Engineering
College
College of Engineering

    

Current Scheduled Teaching*

CSCE 5210.001, Fundamentals of Artificial Intelligence, Spring 2023 Syllabus
CSCE 5210.002, Fundamentals of Artificial Intelligence, Spring 2023 Syllabus
CSCE 5210.600, Fundamentals of Artificial Intelligence, Spring 2023 Syllabus
CSCE 4201.001, Introduction to Artificial Intelligence, Spring 2023 Syllabus
CSCE 5215.002, Machine Learning, Spring 2023 Syllabus
CSCE 5215.003, Machine Learning, Spring 2023 Syllabus
CSCE 5215.600, Machine Learning, Spring 2023 Syllabus
CSCE 5900.824, Special Problems, Spring 2023 Syllabus

* 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 5210.001, Fundamentals of Artificial Intelligence, Fall 2022 Syllabus SPOT
CSCE 5210.002, Fundamentals of Artificial Intelligence, Fall 2022 Syllabus SPOT
CSCE 5210.600, Fundamentals of Artificial Intelligence, Fall 2022 Syllabus SPOT
CSCE 5214.002, Software Development for Artificial Intelligence, Fall 2022 Syllabus SPOT
CSCE 5900.824, Special Problems, Fall 2022 Syllabus
CSCE 5380.001, Data Mining, Summer 10W 2022 Syllabus SPOT
CSCE 5380.002, Data Mining, Summer 10W 2022 SPOT
CSCE 5210.001, Fundamentals of Artificial Intelligence, Spring 2022 Syllabus SPOT
CSCE 4201.001, Introduction to Artificial Intelligence, Spring 2022 Syllabus SPOT
CSCE 5215.002, Machine Learning, Spring 2022 Syllabus SPOT
CSCE 5215.003, Machine Learning, Spring 2022 Syllabus SPOT
CSCE 5215.600, Machine Learning, Spring 2022 Syllabus SPOT
CSCE 5214.001, Software Development for Artificial Intelligence, Spring 2022 Syllabus SPOT
CSCE 5150.001, Analysis of Computer Algorithms, Fall 2021 SPOT
CSCE 5150.003, Analysis of Computer Algorithms, Fall 2021 SPOT
CSCE 5210.001, Fundamentals of Artificial Intelligence, Fall 2021 SPOT
CSCE 5210.008, Fundamentals of Artificial Intelligence, Fall 2021
CSCE 5214.002, Software Development for Artificial Intelligence, Fall 2021 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 Publications

Published Intellectual Contributions

Conference Proceeding
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
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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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