Faculty Profile

Ajita Rattani

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

    

    

      


                          

            
Curriculum Vitae

            

              Click Here to View Faculty CV
            

          

        
                
                

          
Current Scheduled Teaching*


          
No current or future courses scheduled.


          
*
  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 5934.850, Directed Study, Spring 2024 
                    

                    CSCE 6940.950, Individual Research, Spring 2024 
                    

                    CSCE 4205.400, Introduction to Machine Learning, Spring 2024 Syllabus SPOT
                    

                    CSCE 5215.400, Machine Learning, Spring 2024 Syllabus SPOT
                    

                    CSCE 5950.850, Master's Thesis, Spring 2024 
                    

                    CSCE 5934.850, Directed Study, Fall 2023 
                    

                    CSCE 6940.850, Individual Research, Fall 2023 
                    

                    CSCE 5215.003, Machine Learning, Fall 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 Publications



  

  
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. 



	
		
		
	  

	

	

  
    
	

	

	  

		

		   
Awarded Grants



  
  
  	  
          
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).



	
		
		
	  

	

	

	
	

  
  
  

  	

		

		,  
			
				

					
					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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