Faculty Profile

Pavlo Tymoshchuk

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

    

    

      


                          

            
Curriculum Vitae

            

              Click Here to View Faculty CV
            

          

        
                          

            
Education

            



  

  


  
Habilitation, Odesa National Academy of Telecommunications, 2006. 
Major: Telecommunications Engineering
Degree Specialization: Telecommunications Engineering
Dissertation Title: Development of theory and modeling methods of functional                          blocks of radio engineering systems based on implicit integral-differential equations
PhD, Lviv Polytechnic National University, 1992. 
Major: Technical Sciences
Degree Specialization: Electrical Engineering
Dissertation Title: Approximation models and identification of electric circuits
MSc, Lviv Polytechnic National University, 1982. 
Major: Electrical Engineering
Degree Specialization: Electrical Engineering
Dissertation Title: Modeling of an arc steel melting furnace





          

        
                

          
Current Scheduled Teaching*


          
CSCE 5300.004, Introduction to Big Data and Data Science, Summer 2024 
CSCE 5300.006, Introduction to Big Data and Data Science, Summer 2024 
CSCE 5215.006, Machine Learning, Summer 2024 
CSCE 5300.004, Introduction to Big Data and Data Science, Spring 2024 Syllabus
CSCE 5300.005, Introduction to Big Data and Data Science, Spring 2024 Syllabus
CSCE 4205.001, Introduction to Machine Learning, Spring 2024 Syllabus
CSCE 5215.007, Machine Learning, 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 5300.002, Introduction to Big Data and Data Science, Fall 2023 Syllabus SPOT
                    

                    CSCE 5300.003, Introduction to Big Data and Data Science, Fall 2023 Syllabus SPOT
                    

                    CSCE 5300.004, Introduction to Big Data and Data Science, Fall 2023 Syllabus SPOT
                    

                    CSCE 5214.005, Software Development for Artificial Intelligence, Fall 2023 Syllabus SPOT
                    

                    CSCE 5300.004, Introduction to Big Data and Data Science, Summer 10W 2023 Syllabus SPOT
                    

                    CSCE 5300.006, Introduction to Big Data and Data Science, Summer 10W 2023 Syllabus SPOT
                    

                    CSCE 5215.006, Machine Learning, Summer 10W 2023 Syllabus SPOT
                    

                    CSCE 5200.005, Information Retrieval and Web Search, Spring 2023 Syllabus SPOT
                    

                    CSCE 5300.004, Introduction to Big Data and Data Science, Spring 2023 Syllabus SPOT
                    

                    CSCE 5300.005, Introduction to Big Data and Data Science, Spring 2023 Syllabus SPOT
                    

                    CSCE 5200.001, Information Retrieval and Web Search, Fall 2022 Syllabus SPOT
                    

                    CSCE 5200.002, Information Retrieval and Web Search, Fall 2022 Syllabus SPOT
                    

                    CSCE 5300.004, Introduction to Big Data and Data Science, Fall 2022 Syllabus SPOT
                    

                    CSCE 5215.003, Machine 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


  
Abstracts and Proceedings
Tymoshchuk, P.  (2023). A model of tracking control neural network for the known affine continuous-time nonlinear system. ANALIZA, ZASILANIE ORAZ STEROWANIE UKŁADÓW ELEKTRYCZNYCH.  Gliwice-Ustron: Silesian University of Technology. 
Book
Tymoshchuk, P. V., Lobur, M. V. (2020). Principles of Artificial Neural Networks and Their Applications: Tutorial. N/A. 
Tymoshchuk, P.  (2010). Artificial Neural Networks: Tutorial (in Ukrainian). 
Tymoshchuk, P., Lobur, M.  Basic Theory of Neural Network Design: Tutorial (in Ukrainian). 
Book Chapter
Tymoshchuk, P.  (2023). A NEURAL NETWORK TRACKING CONTROL FOR THE KNOWN AFFINE CONTINUOUS-TIME NONLINEAR SYSTEM.  Gliwice: Silesian University of Technology. 
Conference Proceeding
Tymoshchuk, P., Pastyrska,  (2021). A Model Analysis for Embedded Control of Known Continuous-Time Scalar Nonlinear Systems. Proc. of the XVII-th International Conf. on “Perspective Technologies and Methods in MEMS Design”. 
Tymoshchuk, P.  (2021). Design of parallel rank-order filtering system based on neural circuits of discrete-time. Proc. XVIth Int. Conf. “The Experience of Designing and Application of CAD Systems”. 
Tymoshchuk, P.  (2021). Design of Parallel Sorting System Using Discrete-Time Neural Circuit Model. 
Tymoshchuk, P.  (2020). Optimal control for continuous-time scalar nonlinear systems with known dynamics. Other. 
Tymoshchuk, P.  A neural circuit model of adaptive robust tracking control for continuous-time nonlinear systems. 
Tymoshchuk, P.  (2013). A fast analogue K-winners-take-all neural circuit. Other. 
Tymoshchuk, P.  Continuous-time model of analogue K-winners-take-all neural circuit. Other. 
Tymoshchuk, P., Kaszkurewicz, E.  A Winner-take-all circuit based on second order Hopfield neural networks as building blocks. Other. 
Journal Article
Tymoshchuk, P.  (2024). Neural network optimal control for discrete-time nonlinear systems with known internal dynamics. Neural Computing and Applications. Springer. 
Tymoshchuk, P. V., Wunsch, D. C. (2019). Design of a K-winners-take-all model with a binary spike train. IEEE Transactions. N/A. 49(8), 3131-3140.  New York, NY, USA: IEEE. 
Tymoshchuk, P.  (2013). A model of analogue K-winners-take-all neural circuit. Neural Networks. 42(N/A), 44-61.  United Kingdom: Elsevier. 
Tymoshchuk, P.  (2009). A discrete-time dynamic K-winners-take-all neural circuit. Neurocomputing. 22(N/A), 3191-3202.  United Kingdom: Elsevier. 
Tymoshchuk, P., Kaszkurewicz, E.  (2004). A winner-take-all circuit using neural networks as building blocks. Neurocomputing. 64(N/A), 375-396.  United Kingdom: Elsevier. 



	
		
		
	  

	

	

  
    
	

	

	  

		

		   
Awarded Grants



  
  
  	  
          
Contracts, Grants and Sponsored Research  

 	 
	 	 
  
Grant - Research   
Tymoshchuk, P., "A Tracking Control Neural Network for Known Affine Discrete-Time Nonlinear Systems," Sponsored by CAHSI Local Research Experiences for Undergraduates Program, Local, $5000 Funded. (2024 – 2024).
Tymoshchuk, P. (Principal), "Optimal Control Discrete-Time Neural Network for Nonlinear Systems with Known Dynamics," Sponsored by CAHSI Local Research Experiences for Undergraduates Program, Local, $4000 Funded. (March 6, 2023 – May 14, 2023).
Tymoshchuk, P. (Principal), "Spiking K-Winners-Take-All Neural Circuit and its Application for Parallel Rank-Order Filtering and Parallel Sorting," Sponsored by Fulbright Scholar Program, International, $28000 Funded. (August 12, 2015 – August 12, 2015).
Tymoshchuk, P. (Principal), "A Winner-Take-All Circuit Using Second Order Hopfield Neural Networks as Building Blocks," Sponsored by National Council of Scientific and Technological Development of Brazil, National, $15000 Funded. (December 26, 2001 – December 25, 2002).



	
		
		
	  

	

	

	
	

  
  
  

  	

		

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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
      • 
					
    • 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
      • 
					
    • Your involvement in course (doing assignments, attending classes, etc.) was
      • 
				
    
			
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