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Xinrui Cui

Title: Assistant Professor

Department: Computer Science and Engineering

College: College of Engineering

Curriculum Vitae

Curriculum Vitae Link

Education

  • PhD, University of British Columbia, 2020
    Major: Electrical and Computer Engineering
    Dissertation: Interpretability of Deep Convolutional Neural Networks in Image Analysis

Current Scheduled Teaching

CSCE 6940.976Individual ResearchSpring 2025
CSCE 5215.003Machine LearningSpring 2025
CSCE 5950.876Master's ThesisSpring 2025
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 4205.001Introduction to Machine LearningFall 2024 Syllabus SPOT
CSCE 5215.001Machine LearningFall 2024 SPOT
CSCE 5215.600Machine LearningFall 2024 SPOT
CSCE 5215.003Machine LearningSpring 2024 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

    Conference Proceeding

  • Wang, D., Cui, X. (2024). InNeRF: Learning Interpretable Radiance Fields for Generalizable 3D Scene Representation and Rendering.
  • Wang, D., Cui, X., Chen, X., Zou, Z., Shi, T., Salcudean, S., Wang, Z., Ward, R. (2021). Multi-view 3D Reconstruction with Transformers. International Conference on Computer Vision (ICCV).
  • Cui, X., Wang, Z. (2017). Sparse Unmixing based on Feature Pixels for Hyperspectral Imagery.

    • Journal Article

  • Zhang, Z., Liu, A., Gao, Y., Cui, X., Qian, R., Chen, X. (2024). Distilling Invariant Representations with Domain Adversarial Learning for Cross-Subject Children Seizure Prediction.
  • Wang, D., Cui, X., Chen, X., Ward, R., Wang, Z. (2021). Interpreting Bottom-Up Decision-Making of CNNs via Hierarchical Inference. IEEE Transactions on Image Processing.
  • Cui, X., Wang, D., Wang, Z. (2020). CHIP: Channel-wise Disentangled Interpretation of Deep Convolutional Neural Networks.
  • Cui, X., Wang, D., Wang, Z. (2020). Feature-flow Interpretation of Deep Convolutional Neural Networks. IEEE Transactions on Multimedia.
  • Cui, X., Wang, D., Wang, Z. (2019). Multi-Scale Interpretation Model for Convolutional Neural Networks: Building Trust based on Hierarchical Interpretation. IEEE Transactions on Multimedia.
  • Wang, D., Shi, Z., Cui, X. (2018). Robust Sparse Unmixing for Hyperspectral Imagery. IEEE Transactions on Geoscience and Remote Sensing.
  • Wang, D., Cui, X., Xie, F., Jiang, Z., Shi, Z. (2017). Multi-feature Sea-land Segmentation based on Pixel-wise Learning for Optical Remote Sensing Imagery. International Journal of Remote Sensing.
  • Liu, X., Chen, Y., Cui, X., Zeng, M., Yu, R., Wang, G. (2015). Flexible Nanocomposites with Enhanced Microwave Absorption Properties based on Fe3O4/SiO2 Nanorods and Polyvinylidene Fluoride. Journal of Materials Chemistry A.
  • Liu, X., Cui, X., Chen, Y., Zhang, X., Yu, R., Wang, G., Ma, H. (2015). Modulation of Electromagnetic Wave Absorption by Carbon Shell Thickness in Carbon Encapsulated Magnetite Nanospindles-poly(Vinylidene Fluoride) Composites. Carbon.
  • Liu, X., Cui, X., Liu, Y., Yin, Y. (2015). Stabilization of Ultrafine Metal Nanocatalysts on Thin Carbon Sheets. Nanoscale.
  • Liu, X., Cui, X., Chen, X., Yang, N., Yu, R. (2014). Shell-enhanced Photoluminescence and Ferromagnetism of Co : ZnS / Co : ZnO Core-shell Nanostructure. Materials Research Bulletin.
  • Liu, X., Chen, X., Cui, X., Yu, R. (2014). The Structure and Multifunctional Behaviors of Mn − ZnO/Mn − ZnS Nanocomposites. Ceramics International.

Contracts, Grants and Sponsored Research

    Grant - Research

  • Cui, X. (Principal), "Start-up funds," University of North Texas, $50000 Funded. (2024 - 2025).
  • Junker, J.R. (Principal), Compson, Z.G. (Co-Principal), Cui, X. (Co-Principal), "“AutoTaxonomizer”: Developing taxonomic- and morphology-informed neural networks for the analysis and identification of novel macroinvertebrate communities," sponsored by UNT College of Science/UNT College of Engineering, University of North Texas, $10000 Funded. (2024 - 2025).
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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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