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Wael Korani

Title: Assistant Professor

Department: Information Science

College: College of Information

Curriculum Vitae

Curriculum Vitae Link

Education

  • PhD, University of Regina, 2021
    Major: Computer Science
    Specialization: Artificial intelligence
    Dissertation: Mother Tree Optimization for solving continues and discrete optimization problems
  • MSc, Tuskegee university, 2015
    Major: Bioinformatics
    Specialization: Master of science
  • Certificate, Oracle university, 2008
    Major: database
    Specialization: DBA and DBD
  • MSc, Cairo University, 2008
    Major: Electrical Power and Machines Engineering
    Specialization: Control Systems
  • BS, Ain Shams University, 2002
    Major: Electrical Engineering
    Specialization: Electrical Engineering

Current Scheduled Teaching

INFO 5707.020Data Modeling for Information ProfessionalsSpring 2025
DTSC 5501.020Fundamentals of Data ScienceFall 2024
DTSC 5501.402Fundamentals of Data ScienceFall 2024
INFO 6900.042Special ProblemsFall 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

DTSC 5505.004Applied Machine Learning for Data ScientistsSummer 10W 2024 SPOT
INFO 5505.020Applied Machine Learning for Data ScientistsSpring 2024 SPOT
INFO 6900.727Special ProblemsSpring 2024
INFO 5505.020Applied Machine Learning for Data ScientistsFall 2023 SPOT
INFO 6900.042Special ProblemsFall 2023
INFO 5505.001Applied Machine Learning for Data ScientistsSummer 5W2 2023 SPOT
INFO 5709.002Data Visualization and CommunicationSummer 5W1 2023 SPOT
INFO 5505.020Applied Machine Learning for Data ScientistsSpring 2023 SPOT
INFO 5505.001Applied Machine Learning for Data ScientistsFall 2022 SPOT
INFO 5505.020Applied Machine Learning for Data ScientistsFall 2022 SPOT
INFO 5505.001Applied Machine Learning for Data ScientistsSummer 5W2 2022 SPOT
INFO 5709.002Data Visualization and CommunicationSummer 5W1 2022 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

  • Korani, W. (2023). Binary Mother Tree Optimization Algorithm for 0/1 Knapsack Problem. Springer nature.
  • Korani, W., Akbari, H. (2023). Early Detection of Depression and Alcoholism Disorders by EEG Signal.
  • Korani, W., Malek, M. (2022). Discrete Mother Tree Optimization and Swarm Intelligence for Constraint Satisfaction Problems. ICAART.
  • Korani, W., Mouhoub, M. Mother Tree Optimization for Conditional Constraints and Qualitative Preferences. 8. IEEE SSCI 2022.

Contracts, Grants and Sponsored Research

    Grant - Research

  • Malarvizhi Kumar, P. (Principal), Xiao, T. (Co-Principal), Korani, W. (Co-Principal), "Cohort II of the Capacity Building for Research at Minority-Serving Institutions: Infrastructure Research Readiness," sponsored by NSF, Federal, $3000 Funded. (2023 - 2024).
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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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