Faculty Profile

Haili Wang

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

    

    

      


                          

            
Curriculum Vitae

            

              Click Here to View Faculty CV
            

          

        
                          

            
Education

            



  

  


  
PhD, University of North Texas, 2022. 
Major: Computer Science and Engineering
MS, University of North Texas, 2017. 
Major: Computer Science
BBA, University of North Texas, 2013. 
Major: Finance





          

        
                

          
Current Scheduled Teaching*


          
CSCE 5350.001, Fundamentals of Database Systems, Summer 2024 
CSCE 5216.002, Pattern Recognition, Summer 2024 
CSCE 1040.001, Computer Science II, Spring 2024 Syllabus
CSCE 1040.002, Computer Science II, Spring 2024 Syllabus
CSCE 1040.003, Computer Science II, Spring 2024 Syllabus
CSCE 1040.303, Computer Science II, Spring 2024 Syllabus
CSCE 1040.304, Computer Science II, Spring 2024 Syllabus
CSCE 1040.306, Computer Science II, Spring 2024 Syllabus
CSCE 1040.308, Computer Science II, Spring 2024 Syllabus
CSCE 1040.309, Computer Science II, Spring 2024 Syllabus
CSCE 1040.310, Computer Science II, Spring 2024 Syllabus
CSCE 1040.311, Computer Science II, Spring 2024 Syllabus
CSCE 1040.312, Computer Science II, Spring 2024 Syllabus
CSCE 1040.313, Computer Science II, Spring 2024 Syllabus
CSCE 1040.314, Computer Science II, Spring 2024 
CSCE 1040.315, Computer Science II, Spring 2024 Syllabus
CSCE 1040.318, Computer Science II, Spring 2024 Syllabus
CSCE 5320.002, Scientific Data Visualization, 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*


            
                    

                    CSCE 5200.001, Information Retrieval and Web Search, Fall 2023 Syllabus SPOT
                    

                    CSCE 5200.002, Information Retrieval and Web Search, Fall 2023  SPOT
                    

                    CSCE 5200.003, Information Retrieval and Web Search, Fall 2023  SPOT
                    

                    CSCE 5200.600, Information Retrieval and Web Search, Fall 2023  SPOT
                    

                    CSCE 5350.001, Fundamentals of Database Systems, Summer 10W 2023 Syllabus SPOT
                    

                    CSCE 5216.002, Pattern Recognition, Summer 10W 2023 Syllabus SPOT
                    

                    CSCE 1030.003, Computer Science I, Spring 2023 Syllabus SPOT
                    

                    CSCE 1030.302, Computer Science I, Spring 2023  SPOT
                    

                    CSCE 1030.308, Computer Science I, Spring 2023  SPOT
                    

                    CSCE 1030.309, Computer Science I, Spring 2023  SPOT
                    

                    CSCE 1030.311, Computer Science I, Spring 2023  SPOT
                    

                    CSCE 1030.314, Computer Science I, Spring 2023  SPOT
                    

                    CSCE 1040.501, Computer Science II, Spring 2023 Syllabus SPOT
                    

                    CSCE 1040.551, Computer Science II, Spring 2023  SPOT
                    

                    CSCE 5200.001, Information Retrieval and Web Search, Spring 2023 Syllabus SPOT
                    

                    CSCE 5200.006, Information Retrieval and Web Search, Spring 2023 Syllabus SPOT
                    

                    CSCE 2100.004, Foundations of Computing, Fall 2022 Syllabus SPOT
                    

                    CSCE 2100.001, Foundations of Computing, Fall 2020 Syllabus SPOT
                    

                    CSCE 2100.002, Foundations of Computing, Fall 2020 Syllabus SPOT
                    

                    CSCE 2100.004, Foundations of Computing, Fall 2020 Syllabus SPOT
                    

                    CSCE 2100.005, Foundations of Computing, Fall 2020 Syllabus SPOT
                    

                    CSCE 2100.201, Foundations of Computing, Fall 2020  SPOT
                    

                    CSCE 2100.206, Foundations of Computing, Fall 2020  SPOT
                    

                    CSCE 2100.207, Foundations of Computing, Fall 2020  SPOT
                    

                    CSCE 2100.208, Foundations of Computing, Fall 2020  SPOT
                    

                    CSCE 2100.209, Foundations of Computing, Fall 2020  SPOT
                    

                    CSCE 2100.210, Foundations of Computing, Fall 2020  SPOT
                    

                    CSCE 2100.211, Foundations of Computing, Fall 2020  SPOT
                    

                    CSCE 2100.282, Foundations of Computing, Fall 2020  SPOT
                    

                    CSCE 2100.284, Foundations of Computing, Fall 2020  SPOT
                    

                    CSCE 2100.291, Foundations of Computing, Fall 2020  SPOT
                    

                    CSCE 2100.293, Foundations of Computing, Fall 2020  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
Bai, T., Wang, H., Guo, J.  (2022). Online Self-Evolving Anomaly Detection for Reliable Cloud Computing. 2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC). https://ieeexplore.ieee.org/abstract/document/10061785
Wang, H., Guo, J., Ma, X.  (2022). Online Self-Evolving Anomaly Detection in Cloud Computing Environments. ARES 2022. https://arxiv.org/abs/2111.08232
Wang, H., Li, Y., Buckles, B.  (2021). A Variety of Deep Learning Models to Classify Disaster Scene Videos. Proceedings of the International Conference on Science, Innovation and Management (ICSIM). 
Li, Y., Wang, H., Buckles, B.  (2020). Integrating Multiple Deep Learning Models to Classify Disaster Scene Videos. IEEE High Performance Extreme Computing Conference (HPEC). 
Li, Y., Wang, H., Buckles, B.  (2019). Traffic Congestion Assessment Based on Street Level Data for On-Edge Deployment. Proceedings of the 4th ACM/IEEE Symposium on Edge Computing. https://dl.acm.org/doi/abs/10.1145/3318216.3363368



	
		
		
	  

	

	

  
    
	

	

	  

		

		   	
		
		
	  

	

	

	
	

  
  
  

  	

		

		,  
			
				

					
					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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