Skip to main content

Sayed K. Shah

Title: Clinical Assistant Professor

Department: Computer Science and Engineering

College: College of Engineering

Curriculum Vitae

Curriculum Vitae Link

Education

  • PhD, University of Missouri-Kansas City, 2021
    Major: Computer Science
    Specialization: Data Science, Edge Computing, Machine Learning, and Deep Learning
    Dissertation: AI-Based Edge Computing System for Event Based Analytics
  • MS, Queen Mary University of London, 2013
    Major: Computer Science
    Specialization: Networks/PlanetLab
    Dissertation: Geo-Distributed Data Stores

Current Scheduled Teaching

CSCE 5320.003Scientific Data VisualizationSpring 2025
CSCE 5320.004Scientific Data VisualizationSpring 2025
CSCE 4890.726Directed StudyFall 2024
CSCE 5222.002Feature EngineeringFall 2024
CSCE 5310.002Methods in Empirical AnalysisFall 2024
CSCE 5320.001Scientific Data VisualizationFall 2024
CSCE 5320.002Scientific Data VisualizationFall 2024
CSCE 5320.004Scientific Data VisualizationFall 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 5222.001Feature EngineeringSummer 10W 2024 SPOT
CSCE 5934.826Directed StudySpring 2024
CSCE 5290.003Natural Language ProcessingSpring 2024 SPOT
CSCE 5320.003Scientific Data VisualizationSpring 2024 SPOT
CSCE 5320.004Scientific Data VisualizationSpring 2024 SPOT
CSCE 5934.826Directed StudyFall 2023
CSCE 5222.002Feature EngineeringFall 2023 SPOT
CSCE 5215.004Machine LearningFall 2023 SPOT
CSCE 5310.002Methods in Empirical AnalysisFall 2023 SPOT
CSCE 5310.003Methods in Empirical AnalysisFall 2023 SPOT
CSCE 3612.001Embedded Systems DesignSummer 10W 2023 Syllabus SPOT
CSCE 3612.202Embedded Systems DesignSummer 10W 2023 Syllabus SPOT
CSCE 3612.203Embedded Systems DesignSummer 10W 2023 Syllabus
CSCE 5222.001Feature EngineeringSummer 10W 2023 SPOT
CSCE 5290.001Natural Language ProcessingSummer 10W 2023 SPOT
CSCE 5290.001Natural Language ProcessingSpring 2023 SPOT
CSCE 5320.003Scientific Data VisualizationSpring 2023 SPOT
CSCE 5320.004Scientific Data VisualizationSpring 2023 SPOT
CSCE 5430.002Software EngineeringSpring 2023 SPOT
CSCE 5934.826Directed StudyFall 2022
CSCE 5222.002Feature EngineeringFall 2022 SPOT
CSCE 4290.001Introduction to Natural Language ProcessingFall 2022 Syllabus SPOT
CSCE 5310.003Methods in Empirical AnalysisFall 2022 SPOT
CSCE 5290.001Natural Language ProcessingFall 2022 SPOT
CSCE 5214.005Software Development for Artificial IntelligenceFall 2022 SPOT
CSCE 5934.914Directed StudySpring 2022
CSCE 5210.002Fundamentals of Artificial IntelligenceSpring 2022 SPOT
CSCE 5290.001Natural Language ProcessingSpring 2022 SPOT
CSCE 5320.002Scientific Data VisualizationSpring 2022 SPOT
CSCE 5430.002Software EngineeringSpring 2022 SPOT
CSCE 1035.001Computer Programming IFall 2021 Syllabus SPOT
CSCE 1035.306Computer Programming IFall 2021 SPOT
CSCE 5222.002Feature EngineeringFall 2021 SPOT
CSCE 4290.001Introduction to Natural Language ProcessingFall 2021 Syllabus SPOT
CSCE 5290.001Natural Language ProcessingFall 2021 SPOT
CSCE 5290.600Natural Language ProcessingFall 2021 SPOT
CSCE 4010.002Social Issues in ComputingFall 2021 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

  • Gillette, J., Shah, S., Tariq, Z., Algamdi, S. (2022). Data protections for minors with named entity recognition. IEEE. https://ieeexplore.ieee.org/abstract/document/10021086
  • Algamdi, S., Albanyan, A., Shah, S., Tariq, Z. (2022). Twitter Accounts Suggestion: Pipeline Technique SpaCy Entity Recognition. IEEE Access. IEEE. https://ieeexplore.ieee.org/abstract/document/10020570
  • Tariq, Z., Shah, S., Lee, Y. (2021). Automatic Multimodal Heart Disease Classification using Phonocardiogram Signal. IEEE. https://ieeexplore.ieee.org/abstract/document/9378232
  • Shah, S., Tariq, Z., Lee, J., Lee, Y. (2021). Real-Time Machine Learning for Air Quality and Environmental Noise Detection. IEEE. https://ieeexplore.ieee.org/author/37088809485
  • Tariq, Z., Shah, S., Lee, Y. (2021). Multimodal Lung Disease Classification using Deep Convolutional Neural Network. IEEE. https://ieeexplore.ieee.org/abstract/document/9313208
  • Shah, S., Tariq, Z., Lee, Y. (2020). IoT based Urban Noise Monitoring in Deep Learning using Historical Reports. IEEE. https://ieeexplore.ieee.org/abstract/document/9006176
  • Tariq, Z., Shah, S., Lee, Y. (2020). Speech Emotion Detection using IoT based Deep Learning for Health Care. IEEE. https://ieeexplore.ieee.org/abstract/document/9005638
  • Tariq, Z., Shah, S., Lee, Y. (2020). Lung Disease Classification using Deep Convolutional Neural Network. IEEE. https://ieeexplore.ieee.org/abstract/document/8983071
  • Tariq, Z., Shah, S., Lee, Y. (2019). Smart 311 Request System with Automatic Noise Detection for Safe Neighborhood. IEEE. https://ieeexplore.ieee.org/abstract/document/8656773
  • Shah, S., Tariq, Z., Lee, Y. (2019). Audio IoT Analytics for Home Automation Safety. IEEE. https://ieeexplore.ieee.org/abstract/document/8622587

    • Journal Article

  • Tariq, Z., Shah, S.K., Lee, Y. (2022). Feature-Based Fusion Using CNN for Lung and Heart Sound Classification. MDPI. https://www.mdpi.com/1424-8220/22/4/1521
  • Shah, S., Tariq, Z., Lee, Y. (2021). Event-Driven Deep Learning for Edge Intelligence (EDL-EI). Sensors. https://www.mdpi.com/1424-8220/21/18/6023
,
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
CLOSE