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Pavlo Tymoshchuk

Title: Clinical Associate Professor

Department: Computer Science and Engineering

College: College of Engineering

Curriculum Vitae

Curriculum Vitae Link

Education

  • Habilitation, Odesa National Academy of Telecommunications, 2006
    Major: Telecommunications Engineering
    Specialization: Telecommunications Engineering
    Dissertation: 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
    Specialization: Electrical Engineering
    Dissertation: Approximation models and identification of electric circuits
  • MSc, Lviv Polytechnic National University, 1982
    Major: Electrical Engineering
    Specialization: Electrical Engineering
    Dissertation: Modeling of an arc steel melting furnace

Current Scheduled Teaching

CSCE 5300.002Introduction to Big Data and Data ScienceFall 2024
CSCE 5300.003Introduction to Big Data and Data ScienceFall 2024
CSCE 5300.004Introduction to Big Data and Data ScienceFall 2024
CSCE 5950.853Master's ThesisFall 2024
CSCE 5214.005Software Development for Artificial IntelligenceFall 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 5215.006Machine LearningSummer 10W 2024 SPOT
CSCE 5300.004Introduction to Big Data and Data ScienceSpring 2024 SPOT
CSCE 5300.005Introduction to Big Data and Data ScienceSpring 2024 SPOT
CSCE 4205.001Introduction to Machine LearningSpring 2024 Syllabus SPOT
CSCE 5215.007Machine LearningSpring 2024 SPOT
CSCE 5300.002Introduction to Big Data and Data ScienceFall 2023 SPOT
CSCE 5300.003Introduction to Big Data and Data ScienceFall 2023 SPOT
CSCE 5300.004Introduction to Big Data and Data ScienceFall 2023 SPOT
CSCE 5214.005Software Development for Artificial IntelligenceFall 2023 SPOT
CSCE 5300.004Introduction to Big Data and Data ScienceSummer 10W 2023 SPOT
CSCE 5300.006Introduction to Big Data and Data ScienceSummer 10W 2023 SPOT
CSCE 5215.006Machine LearningSummer 10W 2023 SPOT
CSCE 5200.005Information Retrieval and Web SearchSpring 2023 SPOT
CSCE 5300.004Introduction to Big Data and Data ScienceSpring 2023 SPOT
CSCE 5300.005Introduction to Big Data and Data ScienceSpring 2023 SPOT
CSCE 5200.001Information Retrieval and Web SearchFall 2022 SPOT
CSCE 5200.002Information Retrieval and Web SearchFall 2022 SPOT
CSCE 5300.004Introduction to Big Data and Data ScienceFall 2022 SPOT
CSCE 5215.003Machine LearningFall 2022 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 Intellectual Contributions

    Abstracts and Proceedings

  • Tymoshchuk, P. (2024). A MATHEMATICAL MODEL OF TRACKING CONROL NEURAL NETWORK FOR THE KNOWN AFFINE IN THE INPUTS DISCRETE-TIME NONLINEAR SYSTEMS. Analysis, Power Supply and Control of Electrical Systems. Gliwice-Ustron, Silesian University of Technology. https://www.polsl.pl/re3/speto/
  • Tymoshchuk, P. (2023). A model of tracking control neural network for the known affine continuous-time nonlinear system. Analysis, Power Supply and Control of Electrical Systems. Gliwice-Ustron, SILESIAN UNIVERSITY OF TECHNOLOGY, WARSAW UNIVERSITY OF TECHNOLOGY, POLISH SOCIETY OF THEORETICAL AND APPLIED ELECTRICAL ENGINEERING, COMMITTEE ON ELECTRICAL ENGINEERING OF THE POLISH ACADEMY OF SCIENCES, POLISH SECTION OF IEEE. https://www.polsl.pl/re3/en/home-2/speto/

    • 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.

Contracts, Grants and Sponsored Research

    Fellowship

  • 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.V., "Faculty First Flight," sponsored by University of North Texas, University of North Texas, $500 Funded. (2022 - 2023).
  • 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. (2023 - 2023).

    • Grant - Research

  • 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. (2015 - 2016).
  • 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. (2001 - 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
    • 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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