Gurmail Singh

Faculty Associate
Department of Computer Sciences
University of Wisconsin-Madison
Email: Gurmail.Singh@wisc.edu
Office 5536, 1205 University Avenue, Madison, WI 53706

I am a Faculty Associate in the Department of Computer Sciences at the University of Wisconsin-Madison. Previously, I was a research associate at the University of Regina in the Faculty of Engineering and Applied Science (worked with Prof. Kin-Choong Yow), the Department of Computer Science (worked with Prof. Sandra Zilles), and the Department of Mathematics and Statistics (worked with Prof. Allen Herman and Prof. Shaun Fallat). In 2015, I completed my Ph.D. in Mathematics at the University of Regina under the supervision of Prof. Allen Herman and Prof. Shaun Fallat. However, I have been doing research in Computer Science (Machine Learning/Deep Learning and Computational Learning Theory) since May 2018.

Research

Machine learning/artificial intelligence, computational learning theory, computer vision, natural language processing, reality-based algebras, Hopf algebra, and representation theory.

Research Papers in Computer Science and Mathematics

Papers in Computer Science

  1. Gurmail Singh, Stéfano Stefenon, and Kin-Choong Yow. The shallowest transparent and interpretable deep neural network for image recognition. Scientific Reports, vol 15, 13940, April 2025. DOI : 10.1038/s41598-025-92945-2.
  2. Stéfano Stefenon, Laio Seman, Gurmail Singh, and Kin-Choong Yow. Enhanced insulator fault detection using optimized ensemble of deep learning models based on weighted boxes fusion. International Journal of Electrical Power and Energy Systems, vol 168, 110682. July 2025. DOI 10.1016/j.ijepes.2025.110682.
  3. Gurmail Singh. One and one make eleven: An interpretable neural network for image recognition. Knowledge-Based Systems, vol 279 (C), 110926, August 2023. DOI: 10.1016/j.knosys.2023.110926.
  4. Marcelo Corso, Stéfano Stefenon, Gurmail Singh, Fábio Perez, and Valderi Leithardt. Evaluation of visible contamination on power grid insulators using convolutional neural networks. Electrical Engineering, July 2023. DOI: 10.1007/s00202-023-01915-2.
  5. Stéfano Stefenon, Gurmail Singh, Bruno Souza, Roberto Freire, and Kin-Choong Yow. Optimized hybrid YOLOu-Quasi-ProtoPNet for insulators classification. IET Generation, Transmission and Distribution, June 2023. DOI: 10.1049/gtd2.12886.
  6. Gurmail Singh, Stéfano Stefenon, and Kin-Choong Yow. Interpretable visual transmission lines inspections using pseudo-prototypical part network. Machine Vision and Applications, 34 (3), April 2023. DOI: 10.1007/s00138-023-01390-6.
  7. Bruno Souza, Stéfano Stefenon, Gurmail Singh, and Roberto Freire. Hybrid YOLOv5x for classification of insulators defects in transmission lines based on UAV. International Journal of Electrical Power and Energy Systems, vol 148, 108982, June 2023. DOI: 10.1016/j.ijepes.2023.108982.
  8. Gurmail Singh. Think positive: An interpretable neural network for image recognition. Neural Networks, vol 151(C), 178-189, July 2022. DOI: 10.1016/j.neunet.2022.03.034.
  9. Stéfano Stefenon, Gurmail Singh, Kin-Choong Yow, and Alessandro Cimatti. Semi-ProtoPNet deep neural network for the classification of defective power grid distribution structures. Sensors, 22 (13), 4859, June 2022. DOI: 10.3390/s22134859.
  10. Gurmail Singh and Kin-Choong Yow. Object or background: An interpretable deep learning model for COVID-19 detection from CT-scan images. Diagnostics, 11(9), 1732, September 2021. DOI: 10.3390/diagnostics11091732.
  11. Gurmail Singh and Kin-Choong Yow. An interpretable deep learning model for COVID-19 detection with chest x-ray images. IEEE Access, vol. 9, 85198-85208, June 2021. DOI: 10.1109/ACCESS.2021.3087583.
  12. Gurmail Singh and Kin-Choong Yow. These do not look like those: An interpretable deep learning model for image recognition. IEEE Access, vol. 9, pp. 41482-41493, March 2021. DOI: 10.1109/ACCESS.2021.3064838.

Papers in Mathematics

  1. Gurmail Singh. Diagonal torsion matrices associated with modular data. Algebra and Discrete Mathematics, 32(1), 2021, 127-137.
  2. Gurmail Singh. Bialgebra structures on table algebras. Linear and Multilinear Algebra, 69 (12), 2021, 2288-2314.
  3. Allen Herman and Gurmail Singh. Extending table algebras to Hopf algebras. International Electronic Journal of Algebra, 26, 2019, 13-28.
  4. Gurmail Singh. Classification of homogeneous Fourier matrices. Algebra and Discrete Mathematics, 27 (1), 2019, 75-84.
  5. Gurmail Singh. Fourier matrices of small rank. Journal of Algebra Combinatorics Discrete Structures and Applications, 5 (2), 2018, 51-63.
  6. Allen Herman and Gurmail Singh. Orders of torsion units of integral reality-based algebras with positive degree map having rational multiplicities. Journal of Algebra and Its Applications, 17 (1) 2018, 1850015 (12 pages).
  7. Andreas Bachle, Allen Herman, Alexander Konovalov, Leo Margolis, and Gurmail Singh. The status of the Zassenhaus conjecture for small groups. Experimental Mathematics, 27 (4), 2017, 431-436.
  8. Allen Herman and Gurmail Singh. Central torsion units of integral reality-based algebras with a positive degree map. International Electronic Journal of Algebra, 21, 2017, 121-126.
  9. Allen Herman and Gurmail Singh. Revisiting the Zassenhaus Conjecture on torsion units for the integral group rings of small groups. Proceedings - Mathematical Sciences, Indian Academy of Sciences, 125 (2), 2015, 167-172.
  10. Allen Herman and Gurmail Singh. Torsion units of integral C-algebras. JP Journal of Algebra, Number Theory, and Applications, 36 (2), 2015, 141-155.
  11. Allen Herman and Gurmail Singh. On the torsion units of integral adjacency algebras of finite association schemes. Algebra, 14, 2014, 1-5.

Presentations/Talks

Oral Presentations/Talks in Computer Science

  1. Interpretable Deep Learning Models for Image Recognition. Artificial Intelligence Research Seminar. Faculty of Engineering and Applied Science, University of Regina, May 11, 2022.
  2. Multiple Character n-Gram Embeddings to Improve RNN Language Models. Artificial Intelligence Research Seminar. Faculty of Engineering and Applied Science, University of Regina, November 02, 2021.
  3. Negative reasoning process of a deep learning model. Artificial Intelligence Research Seminar. Faculty of Engineering and Applied Science, University of Regina, March 17, 2021.
  4. Classification of images with recognition of prototypes of the images. Artificial Intelligence Research Seminar. Faculty of Engineering and Applied Science, University of Regina, November 25, 2020.
  5. Image prediction with interpretable Deep Learning models. Artificial Intelligence Research Seminar. Faculty of Engineering and Applied Science, University of Regina, September 09, 2020.
  6. An introduction to percetrons. Artificial Intelligence Research Seminar. Faculty of Engineering and Applied Science, University of Regina, July 15, 2020.
  7. Interpretability of Deep Learning models, Artificial Intelligence Research Seminar. Faculty of Engineering and Applied Science, University of Regina, July 1, 2020.
  8. Suicide risk prediction through Machine Learning. Artificial Intelligence Research Seminar. Faculty of Engineering and Applied Science, University of Regina, May 13, 2020.
  9. Random forests and imputation methods. Artificial Intelligence Research Seminar. Faculty of Engineering and Applied Science, University of Regina, March 4, 2020.
  10. A survey of regression trees and decision trees. Artificial Intelligence Research Seminar. Faculty of Engineering and Applied Science, University of Regina, February 26, 2020.
  11. (Invited talk) Encoding the vertices of a hyper cube. Scientific Sessions: Matrix Theory and its Applications. University of Regina, Canada, June 10, 2019.
  12. Subset teaching dimension of maximum concept classes with VC-dimension 2. Computational Learning Theory Seminar. Department of Computer Science, University of Regina, June 4, 2019.
  13. VC-dimension and recursive teaching dimension. Computational Learning Theory Seminar. Department of Computer Science, University of Regina, May 31, 2018.

Oral Presentations/Talks in Mathematics

  1. Central normalized torsion units of integral RBAs and group rings. The Conference on Groups, Rings and Associated Structures. Domain Sol Cress. Spa, Belgium, June 14, 2019.
  2. Non-homogeneous Fourier Matrices. Prairie Discrete Math Workshop. Brandon University, Canada, June 13, 2018.
  3. (Invited talk) C-algebras arising from integral Fourier matrices. Symposium: Linear Algebra Aspects of Association Schemes (ILAS Conference). Iowa State University, USA, July 26, 2017.
  4. Homogeneous Integral Fourier Matrices. Prairie Discrete Math Workshop. Living Skies Conference Centre, Lumsden, Saskatchewan, Canada, June 04, 2017.
  5. Torsion units of integral reality-based algebras with rational multiplicities. Departmental Colloquium. University of Regina, January 20, 2017.
  6. A survey of reality-based algebras. Mathematics Seminar. University of Regina, November 28, 2016.
  7. Normalized torsion units of reality-based algebras with pseudo-standard character. Algebra Seminar. University of Regina, November 08, 2016.
  8. When does a table algebra have rational multiplicities? Departmental Colloquium. University of Regina, October 21, 2016.
  9. Central torsion units of integral reality-based algebras with a positive degree map. Algebra Seminar. University of Regina, August 23, 2016.
  10. Torsion units of C-algebras. Departmental Colloquium. University of Regina, August 29, 2014.
  11. Langrange-type theorems for the torsion units of integral adjacency rings of finite association schemes. Brock International Conference on Groups, Rings and Group Rings. Brock University, Canada, August 01, 2014.
  12. Affine schemes. Algebraic Geometry Seminar. University of Regina, March 21, 2012.

Awards

Professional Volunteer Service

Journal Special Issue Editing

Journal Reviewer

Department Service at the University of Wisconsin-Madison, USA

University Service at the University of Regina, Canada

Teaching Experience in Computer Science and Mathematics (Fall 2007- Fall 2026)

Course Instructor (Summer 2015 to Fall 2026)

Teaching in Computer Sciences

Department of Computer Sciences, University of Wisconsin-Madison, Madison, Wisconsin, USA Department of Computer Science, University of Regina, Regina, Saskatchewan, Canada

Teaching in Mathematics and Statistics

Department of Mathematics and Statistics, University of Regina, Regina, Saskatchewan, Canada

Lab Instructor/Teaching Assistant (Winter 2015)

Department of Mathematics and Statistics, University of Regina, Regina, Saskatchewan, Canada

Assistant Professor, tenure-track (July 2009 to June 2011)

Department of Mathematics, Babbar Akali Memorial Khalsa College, Garhshankar, Punjab, India

I taught the following courses (pre-calculus to graduate level) from July 2009 to June 2011. Graduate Level Courses: Algebra, Real Analysis, Topology, Functional Analysis, Complex Analysis, Numerical Analysis, Number Theory, Differential Geometry, Mechanics, Probability and Statistics. Undergraduate Level Courses: Calculus I, II, III, Vector Calculus, Analysis, Euclidian Geometry, Linear Algebra, Differential and Integral Equations.

College Lecturer, two-year term-position (July 2007 to June 2009)

Department of Mathematics, BLM Girls College, Nawanshahr, Punjab, India

I taught the following courses to undergraduate students from July 2007 to June 2009. Precalculus, Calculus I, II, III, Vector Calculus, Abstract Algebra, Functional Analysis, Complex Analysis, Euclidian Geometry, Linear Algebra, Differential Equations, Introductory Probability, Numerical Analysis, and Mechanics. Also, I taught programming in C and C++ of Numerical Analysis to Computer Applications class.

Schedule

Time Monday Tuesday Wednesday Thursday Friday
 
11:00-12:15
 
 CS320-001 CS320-001
 
1:00-2:15
 
 CS320-002 CS320-002
 
2:30-4:30
 
 Office Hours Office Hours