Faculty Profile

Zeenat Tariq

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

    

Education

PhD, University of Missouri Kansas City, 2021.
Major: Computer Science
Degree Specialization: Data Science, Machine learning
Dissertation Title: Design of Multi-Modality Deep Fusion Architecture for Deep Acoustic Analytics
MS, National University of Sciences and Technology, 2015.
Major: Computational Science and Engineering
Degree Specialization: Computer communication systems
Dissertation Title: Energy Efficient Load Balancing in Computational Grid

Current Scheduled Teaching*

CSCE 5300.006, Introduction to Big Data and Data Science, Spring 2023
CSCE 5300.008, Introduction to Big Data and Data Science, Spring 2023
CSCE 5290.003, Natural Language Processing, Spring 2023
CSCE 5320.005, Scientific Data Visualization, Spring 2023

* 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 5934.827, Directed Study, Fall 2022
CSCE 5300.002, Introduction to Big Data and Data Science, Fall 2022 SPOT
CSCE 5290.002, Natural Language Processing, Fall 2022 SPOT
CSCE 5290.003, Natural Language Processing, Fall 2022 SPOT
CSCE 5934.845, Directed Study, Summer 10W 2022
CSCE 5300.002, Introduction to Big Data and Data Science, Summer 10W 2022 SPOT
CSCE 5300.003, Introduction to Big Data and Data Science, Summer 10W 2022 SPOT
CSCE 5320.001, Scientific Data Visualization, Summer 8W2 2022 SPOT
CSCE 5300.003, Introduction to Big Data and Data Science, Spring 2022 SPOT
CSCE 5300.006, Introduction to Big Data and Data Science, Spring 2022 SPOT
CSCE 4600.001, Introduction to Operating Systems, Spring 2022 Syllabus SPOT
CSCE 4600.002, Introduction to Operating Systems, Spring 2022 Syllabus SPOT
CSCE 4600.201, Introduction to Operating Systems, Spring 2022 SPOT
CSCE 4600.202, Introduction to Operating Systems, Spring 2022 SPOT
CSCE 4600.203, Introduction to Operating Systems, Spring 2022 SPOT
CSCE 4600.204, Introduction to Operating Systems, Spring 2022 SPOT
CSCE 4600.205, Introduction to Operating Systems, Spring 2022 SPOT
CSCE 4600.206, Introduction to Operating Systems, Spring 2022 SPOT
CSCE 5320.001, Scientific Data Visualization, Spring 2022 SPOT
CSCE 2100.002, Foundations of Computing, Fall 2021 Syllabus SPOT
CSCE 2100.003, Foundations of Computing, Fall 2021 Syllabus SPOT
CSCE 2100.004, Foundations of Computing, Fall 2021 Syllabus SPOT
CSCE 2100.202, Foundations of Computing, Fall 2021 SPOT
CSCE 2100.203, Foundations of Computing, Fall 2021 SPOT
CSCE 2100.204, Foundations of Computing, Fall 2021 SPOT
CSCE 2100.206, Foundations of Computing, Fall 2021 SPOT
CSCE 2100.207, Foundations of Computing, Fall 2021 SPOT
CSCE 2100.208, Foundations of Computing, Fall 2021 SPOT
CSCE 2100.211, Foundations of Computing, Fall 2021 SPOT
CSCE 2100.212, Foundations of Computing, Fall 2021
CSCE 2100.213, Foundations of Computing, Fall 2021 SPOT
CSCE 5290.002, Natural Language Processing, 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
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
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