Faculty Profile

Haihua Chen

Title
Clinical Assistant Professor
Department
Information Science
College
College of Information

    

    

      


                          

            
Curriculum Vitae

            

              Click Here to View Faculty CV
            

          

        
                
                

          
Current Scheduled Teaching*


          
INFO 5810.002, Data Analysis and Knowledge Discovery, Summer 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*


            
                    

                    INFO 5731.020, Computational Methods for Information Systems, Spring 2023  SPOT
                    

                    INFO 5810.001, Data Analysis and Knowledge Discovery, Spring 2023  SPOT
                    

                    INFO 5810.002, Data Analysis and Knowledge Discovery, Spring 2023  SPOT
                    

                    INFO 5731.020, Computational Methods for Information Systems, Fall 2022  SPOT
                    

                    INFO 5810.001, Data Analysis and Knowledge Discovery, Fall 2022  SPOT
                    

                    INFO 5810.002, Data Analysis and Knowledge Discovery, Fall 2022  SPOT
                    

                    INFO 5810.002, Data Analysis and Knowledge Discovery, Summer 10W 2022  SPOT
                    

                    INFO 5502.002, Principles and Techniques for Data Science, Summer 5W2 2022  SPOT
                    

                    INFO 5731.002, Computational Methods for Information Systems, Spring 2022  SPOT
                    

                    INFO 5502.004, Principles and Techniques for Data Science, Spring 2022  SPOT
                    

                    INFO 5731.002, Computational Methods for Information Systems, Fall 2021  SPOT
                    

                    INFO 5731.002, Computational Methods for Information Systems, Spring 2021 Syllabus SPOT
                    

                    INFO 5731.203, Computational Methods for Information Systems, Spring 2021 Syllabus SPOT
                    

                    INFO 5731.202, Computational Methods for Information Systems, Fall 2020 Syllabus SPOT
                    

                    INFO 5731.002, Computational Methods for Information Systems, Spring 2020 Syllabus
                    

                    INFO 4206.001, Information Retrieval Systems, Spring 2019 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


  
Conference Proceeding
Qin, C., Y. Y., Chen, H., Ding, J.  (2021). A Comparison Study of Machine Learning and Deep Learning for Legal Contract Understanding. JURISIN 2021: 15 Intl. Workshop on Juris-informatics. 
Chen, J., Chen, H., Tang, M.  (2020). An Ontology-based Semantic Information Retrieval System. IEEE. 
Chen, J., Chen, H., Tang, M.  (2020). Smart bookshelf for library book management. Proceedings of the Association for Information Science and Technology, 2020.. 
Chen, H., Cao, G., Chen, J., Ding, J.  (2019). A Practical Framework for Evaluating the Quality of Knowledge Graph. Knowledge Graph and Semantic Computing: Knowledge Computing and Language Understanding. 12.  Singapore: Springer. 
Journal Article
Tran, N., Chen, H., Bhuyen, J., Ding, J.  (2022). Data Curation and Quality Evaluation for Machine Learning-Based Cyber Intrusion Detection. IEEE Access. 10, 121900 - 121923. IEEE. 
Chen, H., Pieptea, L., Ding, J.  (2022). Construction and Evaluation of a High-Quality Corpus for Legal Intelligence Using Semiautomated Approaches. IEEE Transactions on Reliability. 71(2), 1-17. IEEE. 
Chen, H., Wu, L., Chen, J., Lu, W., Ding, J.  (2021). A comparative study of automated legal text classification using random forests and deep learning. Information Processing & Management. 59(2), . Elsevier. 
Tran, N., Chen, H., Jiang, J., Bhuyan, J., Ding, J.  (2021). Effect of Class Imbalance on the Performance of Machine Learning-based Network Intrusion Detection. International Journal of Performability Engineering. 17(9), . Totem. 
Cartwright, A. D., Carey, C. D., Chen, H.  (2021). Multi-tiered intensive supervision: A culturally-informed method of clinical supervision. Teaching and Supervision in Counseling. https://trace.tennessee.edu/tsc/
Chen, H., Chen, J., Ding, J.  (2021). Data Evaluation and Enhancement for Quality Improvement of Machine Learning. IEEE Transactions on Reliability. 70(2), . IEEE. 
Chen, J., Chen, H., Tang, M.  (2020). An ontology-improved vector space model for semantic retrieval. The Electronic Library. 
Chen, J., Lu, W., Chen, H.  (2019). Result Diversification in Image Retrieval Based on Semantic Distance.  Denton:. 
Cui, J., Ma, Y., Zhang, J., Chen, H., Fang, R.  (1996). Growth and characterization of diamond film on aluminum nitride. Materials Research Bulletin. 31(7), 781--785. Pergamon. 



	
		
		
	  

	

	

  
    
	

	

	  

		

		   
Awarded Grants



  
  
  	  
          
Contracts, Grants and Sponsored Research  

 	 
	 	 
  
Sponsored Research   
Ding, J., Ludi, S. A., Fu, S., Kinshuk, K., Chen, H., "Developing a High-Quality Academic Environment for Broadening Participation of Hispanic Students in Computing," Sponsored by NSF, Federal, $499517 Funded. (November 1, 2022 – October 31, 2025).



	
		
		
	  

	

	

	
	

  
  
  

  	

		

		,  
			
				

					
					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
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    • 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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