Faculty Profile

Nassim Sohaee

Title
Clinical Associate Professor
Department
Information Technology and Decision Sciences
College
College of Business
Clinical Associate Professor
Information Technology and Decision Sciences
College of Business

    

    

      


                          

            
Curriculum Vitae

            

              Click Here to View Faculty CV
            

          

        
                          

            
Education

            



  

  


  
PhD, University of Texas at Dallas, 2009. 
Major: Computer Science
Dissertation Title: Optimization in design of underwater sensor networks
PhD, University Texas at Arlington, 2003. 
Major: Mathematics
Dissertation Title: Upward Embedding of Digraphs on the Surfaces





          

        
                

          
Current Scheduled Teaching*


          
DSCI 5240.004, Data Mining and Machine Learning for Business, Spring 2024 Syllabus
BCIS 5110.002, Programming Languages for Business Analytics, Spring 2024 Syllabus
BCIS 5110.004, Programming Languages for Business Analytics, Spring 2024 Syllabus


          
*
  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*


            
                    

                    DSCI 5240.002, Data Mining and Machine Learning for Business, Fall 2023 Syllabus SPOT
                    

                    DSCI 5240.007, Data Mining and Machine Learning for Business, Fall 2023 Syllabus SPOT
                    

                    BCIS 5110.501, Programming Languages for Business Analytics, Fall 2023 Syllabus SPOT
                    

                    MSCI 6900.703, Special Problems, Fall 2023  SPOT
                    

                    BCIS 5110.001, Programming Languages for Business Analytics, Summer 5W2 2023 Syllabus SPOT
                    

                    DSCI 5240.004, Data Mining and Machine Learning for Business, Spring 2023 Syllabus SPOT
                    

                    BCIS 5110.002, Programming Languages for Business Analytics, Spring 2023 Syllabus SPOT
                    

                    BCIS 5110.004, Programming Languages for Business Analytics, Spring 2023 Syllabus SPOT
                    

                    MSCI 6900.756, Special Problems, Spring 2023 
                    

                    DSCI 5240.005, Data Mining and Machine Learning for Business, Fall 2022 Syllabus SPOT
                    

                    DSCI 5240.006, Data Mining and Machine Learning for Business, Fall 2022 Syllabus SPOT
                    

                    BCIS 5110.005, Programming Languages for Business Analytics, Fall 2022 Syllabus SPOT
                    

                    BCIS 5110.006, Programming Languages for Business Analytics, Fall 2022  SPOT
                    

                    BCIS 5110.008, Programming Languages for Business Analytics, Fall 2022 Syllabus SPOT
                    

                    DSCI 5240.001, Data Mining and Machine Learning for Business, Spring 2022 Syllabus SPOT
                    

                    DSCI 5240.004, Data Mining and Machine Learning for Business, Spring 2022 Syllabus SPOT
                    

                    BCIS 5110.002, Programming Languages for Business Analytics, Spring 2022 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 Publications



  

  
Published Intellectual Contributions


  
Book Chapter
Sohaee, N.  (2023). Constructing renewable energy systems using big data applications. Other. Handbook of Smart Energy Systems. pages 347-359. Springer Cham. 
Journal Article
Sohaee, N., Bohluli, S.  (2024). Nonlinear Analysis of the Effects of Socioeconomic, Demographic, and Technological Factors on the Number of Fatal Traffic Accidents. 10(1), 18.  Basel: MDPI. https://www.mdpi.com/2313-576X/10/1/11
Sohaee, N.  (2023). Error and Optimism Bias Regularization. Other. 10(8), . Springer. https://journalofbigdata.springeropen.com/articles/10.1186/s40537-023-00685-9
Sohaee, N.  (2022). Deep learning model for express lane traffic forecasting. Other. 3(2), 129-135. 



	
		
		
	  

	

	

  
    
	

	

	  

		

		   
Awarded Grants



  
  
  	  
          
Contracts, Grants and Sponsored Research  

 	 
	 	 
  
Contract   
Sohaee, N. (Co-Principal), "Traffic and Revenue study for SH580 and SH680 express lanes," Sponsored by Alameda County, California, Local, $40000 Funded. (January 1, 2020 – August 31, 2020).
Sohaee, N. (Supporting), "North Texas Tollway Authority (NTTA) on traffic and revenue analysis of Special Project System," Sponsored by North Texas Tollway Authority, Local, $20000 Funded. (January 1, 2018 – September 1, 2018).



	
		
		
	  

	

	

	
	

  
  
  

  	

		

		,  
			
				

					
					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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