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Ajita Rattani

Title: Assistant Professor

Department: Computer Science and Engineering

College: College of Engineering

Curriculum Vitae

Curriculum Vitae Link

Current Scheduled Teaching

CSCE 4205.002Introduction to Machine LearningSpring 2025
CSCE 5215.002Machine LearningSpring 2025
CSCE 5934.850Directed StudyFall 2024
CSCE 6940.850Individual ResearchFall 2024
CSCE 5215.003Machine LearningFall 2024
CSCE 5950.850Master's ThesisFall 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 5934.850Directed StudySpring 2024
CSCE 6940.950Individual ResearchSpring 2024
CSCE 4205.400Introduction to Machine LearningSpring 2024 Syllabus SPOT
CSCE 5215.400Machine LearningSpring 2024 SPOT
CSCE 5950.850Master's ThesisSpring 2024
CSCE 5934.850Directed StudyFall 2023
CSCE 6940.850Individual ResearchFall 2023
CSCE 5215.003Machine LearningFall 2023 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

    Book Chapter

  • Rattani, A., Katamneni, V. (2023). An Examination of Fairness of Face and Periocular Recognition at NIR Spectrum. Springer.
  • Rattani, A., Ramachandran, S. Demographic Fairness and Accountability of Audio and Video-based Unimodal and Bi-modal Deepfake Detectors.

    • Conference Proceeding

  • Rattani, A., Nadimpalli, A.V. (2023). Facial Forgery-based Deepfake Detection using Fine-Grained Features. Florida, IEEE.
  • Rattani, A., Katamneni, V. (2023). MIS-AVoiDD: Modality Invariant and Specific Representation for Audio-Visual Deepfake Detection.
  • Rattani, A., Aarotale, P. (2023). PatchBMI-Net: Lightweight Facial Patch-based Ensemble for BMI Prediction. Turkey, Istambul, IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

    • Journal Article

  • Rattani, A. (2023). A novel approach for bias mitigation of gender classification algorithms using consistency regularization. Image and Vision Computing Journal. Elsevier.
  • Rattani, A. (2023). ProActive DeepFake Detection Using GAN-Based Visible Watermarking. ACM Transactions on Multimedia Computing, Communications, and Applications. 27. New York, ACM.
  • Rattani, A., Demissie, Z. The significance of volcanic segments and rifts in faults characterization within the Amagmatic graben of the Afar Depression, Ethiopia. Elsevier.

Contracts, Grants and Sponsored Research

    Grant - Research

  • Rattani, A. (Principal), "Probing Fairness of Ocular Biometrics Methods Across Demographic Variations," sponsored by NSF, Federal, $16000 Funded. (2024 - 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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