Faculty Profile

Tong Shu

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

    

    

      


                          

            
Curriculum Vitae

            

              Click Here to View Faculty CV
            

          

        
                          

            
Education

            



  

  


  
PhD, New Jersey Institute of Technology, 2017. 
Major: Computer Science
Degree Specialization: Parallel and Distributed Computing
Dissertation Title: Performance Optimization and Energy Efficiency of Big-data Computing Workflows





          

        
                

          
Current Scheduled Teaching*


          
CSCE 4110.002, Algorithms, Spring 2024 Syllabus
CSCE 4890.760, Directed Study, Spring 2024 
CSCE 6940.960, Individual Research, Spring 2024 


          
*
  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 5218.001, Deep Learning, Fall 2023 Syllabus SPOT
                    

                    CSCE 4890.760, Directed Study, Fall 2023 
                    

                    CSCE 6940.860, Individual Research, Fall 2023 
                    

                    CSCE 5610.003, Computer System Architecture, Spring 2023 Syllabus SPOT
                    

                    CSCE 4610.001, Computer Systems Architecture, Spring 2023 Syllabus SPOT
                    

                    CSCE 5934.917, Directed Study, Spring 2023 
                    

                    CSCE 6940.960, Individual Research, Spring 2023 
                    

                    CSCE 5218.001, Deep Learning, Fall 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


  
Conference Proceeding
Zhang, Y., Pandey, D., Wu, D., Kundu, T., Li, R., Shu, T.  (2023). Accuracy-Constrained Efficiency Optimization and GPU Profiling of CNN Inference for Detecting Drainage Crossing Locations. 1780-1788.  Denver, CO, USA: Workshops of ACM International Conference for High Performance Computing, Networking, Storage and Analysis (SC-W). https://dl.acm.org/doi/10.1145/3624062.3624260
Li, Y., Baik, J., Rahman, M., Anagnostopoulos, I., Li, R., Shu, T.  (2023). Pareto Optimization of CNN Models via Hardware-Aware Neural Architecture Search for Drainage Crossing Classification on Resource-Limited Devices. 1767-1775.  Denver, CO, USA: Workshops of ACM International Conference for High Performance Computing, Networking, Storage and Analysis (SC-W). https://dl.acm.org/doi/10.1145/3624062.3624258
Kundu, T., Shu, T.  (2023). HIOS: Hierarchical Inter-Operator Scheduler for Real-Time Inference of DAG-Structured Deep Learning Models on Multiple GPUs. 95-106.  Santa Fe, NM, USA: IEEE International Conference on Cluster Computing (Cluster). https://ieeexplore.ieee.org/document/10319967
Caldwell, J., Feng, W., Byun, M., Albert, M. V., Shu, T., Ji, Y.  (2023). Exploring Power and Thermal Dynamics in the Summit Supercomputer: A Data Visualization Study. 7th Annual Smoky Mountains Computational Sciences Data Challenge (SMCDC). 
Haleem, Y., Wagenvoord, I., Wei, Q., Xiao, T., Shu, T., Ji, Y.  (2023). Understanding Nationwide Power Outage and Restoration for Future Prediction. 7th Annual Smoky Mountains Computational Sciences Data Challenge (SMCDC). 
Shu, T., Guo, Y., Wozniak, J., Ding, X., Foster, I., Kurc, T.  (2021). Bootstrapping In-Situ Workflow Auto-Tuning via Combining Performance Models of Component Applications. 28: 1-15.  St. Louis, MO, USA: ACM/IEEE International Conference for High Performance Computing, Networking, Storage, and Analysis (SC). https://dl.acm.org/doi/10.1145/3458817.3476197
Journal Article
Foster, I., Ainsworth, M., Bessac, J., Cappello, F., Choi, J., Di, S., Di, Z., Gok, A., Guo, H., Huck, K., Kelly, C., Klasky, S., Kleese Van Dam, K., Liang, X., Mehta, K., Parashar, M., Peterka, T., Pouchard, L., Shu, T., Tugluk, O., Van Dam, H., Wan, L., Wolf, M., Wozniak, J., Xu, W., Yakushin, I., Yoo, S., Munson, T.  (2021). Online Data Analysis and Reduction: An Important Co-design Motif for Extreme-scale Computers. International Journal of Higher Performance Computing Applications. 35(6), 617-635. SAGE. 
Shu, T., Wu, C.  (2020). Energy-efficient Mapping of Large-scale Workflows under Deadline Constraints in Big Data Computing Systems. Future Generation Computer Systems. 110, 515-530. Elsevier. https://www.sciencedirect.com/science/article/abs/pii/S0167739X17300468
Poster
Shu, T., Guo, Y., Wozniak, J., Ding, X., Foster, I., Kurc, T.  (2021). POSTER: In-situ Workflow Auto-tuning through Combining Component Models. 467–468. ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP) 2021. https://dl.acm.org/doi/10.1145/3437801.3441615



	
		
		
	  

	

	

  
    
	

	

	  

		

		   
Awarded Grants



  
  
  	  
          
Contracts, Grants and Sponsored Research  

 	 
	 	 
  
Grant - Research   
Shu, T. (Principal), "Collaborative Research: CyberTraining: Pilot: Research Workforce Development for Deep Learning Systems in Advanced GPU Cyberinfrastructure," Sponsored by National Science Foundation, Federal, $201262 Funded. (December 1, 2022 – November 30, 2024).



	
		
		
	  

	

	

	
	

  
  
  

  	

		

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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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