Faculty Profile

Yuede Ji

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

    

    

      


                          

            
Curriculum Vitae

            

              Click Here to View Faculty CV
            

          

        
                          

            
Education

            



  

  


  
PhD, The George Washington University, 2021. 
Major: Computer Engineering
Degree Specialization: Cybersecurity and High-Performance Computing
Dissertation Title: High-Performance Graph Computing and Application in Cybersecurity
MS, Jilin University, 2015. 
Major: Computer Science
Degree Specialization: Cybersecurity
Dissertation Title: Host-Based Botnet Detection
BE, Jilin University, 2012. 
Major: Software Engineering
Degree Specialization: Software Engineering
Dissertation Title: Research of Host-based Bot Detection





          

        
                

          
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 6933.001, Advanced Topics in Computer Science and Engineering, Spring 2023  SPOT
                    

                    CSCE 4890.762, Directed Study, Spring 2023 
                    

                    CSCE 6940.917, Individual Research, Spring 2023 
                    

                    CSCE 5150.002, Analysis of Computer Algorithms, Fall 2022 Syllabus SPOT
                    

                    CSCE 4890.709, Directed Study, Fall 2022 
                    

                    CSCE 5934.809, Directed Study, Fall 2022 
                    

                    CSCE 6940.809, Individual Research, Fall 2022 
                    

                    CSCE 6940.917, Individual Research, Spring 2022 
                    

                    CSCE 5565.001, Secure Software Systems, Spring 2022 Syllabus SPOT
                    

                    CSCE 5150.002, Analysis of Computer Algorithms, Fall 2021 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
Fu, Q., Ji, Y., Huang, H.  (2022). TLPGNN: A Lightweight Two-Level Parallelism Paradigm for Graph Neural Network Computation on GPU. ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC). https://dl.acm.org/doi/abs/10.1145/3502181.3531467
He, H., Ji, Y., Huang, H.  (2022). Illuminati: Towards Explaining Graph Neural Networks for Cybersecurity Analysis. IEEE European Symposium on Security and Privacy. https://ieeexplore.ieee.org/document/9797387
Ji, Y., Huang, H.  NestedGNN: Detecting Malicious Network Activity with Nested Graph Neural Networks. IEEE. 
Ji, Y., Elsabagh, M., Johnson, R., Stavrou, A.  (2021). DEFInit: An Analysis of Exposed Android Init Routines. USENIX Security. 
Ji, Y., Cui, L., Huang, H.  (2021). Vestige: Identifying Binary Code Provenance for Vulnerability Detection. The 19th International Conference on Applied Cryptography and Network Security (ACNS). 
Ji, Y., Cui, L., H.  (2021). BugGraph: Differentiating Source-Binary Code Similarity with Graph Triplet-Loss Network. The ACM Asia Conference on Computer and Communications Security (AsiaCCS). 
Journal Article
Ji, Y., Liu, H., Hu, Y., Huang, H.  iSpan: Parallel Identification of Strongly Connected Components with Spanning Trees. ACM. 
Shang, L., Guo,, Ji, Y., Li, Q.  (2021). Discovering Unknown Advanced Persistent Threat Using Shared Features Mined by Neural Networks. Computer Networks. 
Poster
Wang, L., Malladi, A., Ji, Y.  Efficient Sparse Deep Neural Network Computation on GPU. ACM/IEEE International Conference for High Performance Computing, Networking, Storage, and Analysis (SC). 



	
		
		
	  

	

	

  
    
	

	

	  

		

		   
Awarded Grants



  
  
  	  
          
Contracts, Grants and Sponsored Research  

 	 
	 	 
  
Grant - Research   
Ji, Y., "Scalable and Efficient Computation of Graph Neural Networks on GPUs," Sponsored by Google, Private, $5000 Funded. (September 1, 2021 – August 31, 2022).
Grant - Teaching   
Ji, Y., "Analysis of Computer Algorithms," Sponsored by Google, Private, $5350 Funded. (August 27, 2022 – August 29, 2023).
Ji, Y., "Analysis of Computer Algorithms," Sponsored by Google, Private, $2900 Funded. (October 1, 2021 – August 23, 2022).



	
		
		
	  

	

	

	
	

  
  
  

  	

		

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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
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    • The course content was
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    • The instructor’s contribution to the course was
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    • The instructor’s effectiveness in teaching the subject matter was
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  • 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
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    • The intellectual challenge presented was
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    • The amount of effort you put into this course was
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    • The amount of effort to succeed in this course was
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    • Your involvement in course (doing assignments, attending classes, etc.) was
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