PGCert Learning & Teaching in Higher Education
Teesside University, United Kingdom
Teesside University London · United Kingdom
Jaspreet Singh
Tutor in Computing · Computer Vision and Machine Learning Researcher
PhD, FHEA · UK Global Talent
About
Biography
I am a Computing Tutor at Teesside University London and a researcher in computer vision and machine learning. My research focuses on invariant and equivariant neural networks, 2D and 3D vision, and visual methods for digital agriculture. I combine method development with applied research in plant phenotyping, intelligent sensing and AI-supported learning.
Areas of specialism
Computer Vision · Machine and Deep Learning · Equivariant Neural Networks · Explainable AI · Digital Image Analysis · 2D/3D Computer Vision · Plant Phenotyping · Pattern Recognition · Natural Language Processing · AI in Education
University roles and responsibilities
- Deliver undergraduate and postgraduate lectures, tutorials and practical sessions in computing.
- Design learning activities, assessments and teaching resources aligned with module outcomes.
- Supervise MSc research projects and support students’ research methods, technical development and academic writing.
- Contribute to marking, moderation, student support and module improvement.
My qualifications
PhD in Computer Science
Punjabi University, Patiala, India · Thesis: “Object Recognition Using Rotation Invariant Shape and Color Features”
M.Phil in Computer Science
Punjabi University, Patiala, India · GPA 8.5/10
Master of Computer Applications (MCA)
Punjabi University, Patiala, India · 72%
Bachelor of Computer Applications (BCA)
Punjabi University, Patiala, India · 70%
FHEA Fellow of the Higher Education Academy · 2025UGC-NET Computer Science · India
Previous roles
Postdoctoral Research Associate in Computer Vision
University of Lincoln, United Kingdom · UKRI NUE-Profits project; 3D phenotyping, reconstruction, segmentation, biomass and LAI estimation.
Project Officer · Indic OCR
Punjabi University, Patiala, India · Ministry of Electronics and Information Technology-funded research on Optical Character Recognition for Indic languages.
Computer Science Teaching
Punjabi University, Patiala, India · Undergraduate and postgraduate teaching in AI, data structures, decision support systems, management information systems and digital image processing.
Affiliations and memberships
- British Machine Vision Association (BMVA)
- Society for Pattern Recognition
- Novel and Underutilised Crop Network (NUCNet)
- Research collaboration with Wageningen University & Research
Research networks
Indic OCR project collaboration included IIIT Hyderabad, IIT Delhi, C-DAC, IIT Bombay, IISc Bangalore and IIT Jodhpur. Agri-tech work involved academic and industry partners and engagement with farmers through workshops and demonstrations.
Supervision
Postgraduate and research supervision
- Teesside University London: MSc research project supervision in Computing.
- University of Lincoln: CDT MSc project on predictive modelling for tomato grafting.
- University of Lincoln: L-CAS summer research internship on computer vision for wheat segmentation.
- Examiner for MSc dissertations.
Teaching
Teaching experience spans lectures, tutorials, practical sessions and academic support for undergraduate and postgraduate computing students. Earlier teaching at Punjabi University included Data Structures, Artificial Intelligence, Decision Support Systems, Management Information Systems and Digital Image Processing, with laboratory work using C/C++, MATLAB/Octave and Python.
Research showcase
Digital system to measure wheat biomass and LAI
Work from the UKRI-funded NUE-Profits project on non-destructive crop phenotyping using computer vision and 3D reconstruction.
Academic and professional service
- Guest Editor, Journal of Imaging special issue “Advances in Intelligent Sensing and Navigation”.
- Area Chair, IEEE International Joint Conference on Neural Networks (IJCNN 2025).
- Reviewer for CVPR, BMVC, WACV, ACM Multimedia, Pattern Recognition, AISTATS, NeurIPS, AAAI and ECCV.
- Research engagement through NUE Fine Farmer Workshops, demonstrations and panel discussions.
Selected work & full record
Publications and research in progress.
Browse journal articles, conference papers, preprints and current submissions. Links open where available.
Showing all publications
Kaur, M., Singh, J. & Saggoo, M. I. S. Cytotype-Dependent Meiotic Behaviour and Morphometric Differentiation in Asplenium trichomanes L. from the Western Himalaya
Caryologia: International Journal of Cytology, Cytosystematics and Cytogenetics
Kaur, M. & Singh, J. Population-Level Meiotic Instability and Fertility Variation in Pteris vittata L. from the Western Himalaya
Cytologia
Kaya, M., Singh, J., Yilmaz, A., Guevara, L., Cielniak, G. & Valluru, R. Non-Destructive Biomass and SPAD Estimation Based on 3D Photogrammetry for Faba Bean
10th International Conference on Energy Efficiency and Agricultural Engineering, pp. 1–6
Kulathunga, G., Yilmaz, A., Huang, Z., Hroob, I., Guevara, L., Singh, J., Cielniak, G. & Hanheide, M. Navigating Narrow Spaces: A Comprehensive Framework for Agricultural Robots
IEEE Robotics and Automation Letters
Singh, J. & Cielniak, G. Permutation-Invariant Polar Harmonic Pooling for Point-Based Neural Networks
British Machine Vision Conference (BMVC 2025)
Singh, J. & Cielniak, G. Non-Destructive Biomass Estimation Based on 3D Reconstruction from a Handheld Camera
IEEE 20th International Conference on Automation Science and Engineering (CASE 2024)
Singh, J. & Singh, C. Color Object Classification Using Multi-Channel Zernike Moments-Based Rotation Invariant Bag-of-Visual-Words and Deep Convolutional Neural Networks
Information Fusion, 97, 101823
Singh, J., Singh, C. & Rana, A. Orthogonal Transforms for Learning Invariant Representations in Equivariant Neural Networks
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2023)
Singh, J. & Singh, C. Learning Invariant Representations for Equivariant Neural Networks Using Orthogonal Moments
International Joint Conference on Neural Networks (IJCNN 2022)
Singh, J. & Singh, C. Bag of Groups of Convolutional Features Model for Visual Object Recognition
IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2021)
Singh, C. & Singh, J. Geometrically Invariant Color, Shape and Texture Features for Object Recognition Using Multiple Kernel Learning Classification Approach
Information Sciences, 484, 135–152
Singh, C. & Singh, J. Multi-Channel versus Quaternion Orthogonal Rotation Invariant Moments for Color Image Representation
Digital Signal Processing, 78, 376–392
Singh, C. & Singh, J. Quaternion Generalized Chebyshev-Fourier and Pseudo-Jacobi-Fourier Moments for Color Object Recognition
Optics & Laser Technology, 106, 234–250
Singh, C. & Singh, J. On Rotation Invariance of Orthogonal Moments and Transforms for Object Recognition: A Review
Signal Processing
Singh, J. & Singh, C. Multi-Channel Generalized Pseudo-Jacobi-Fourier Moments for Color Image Reconstruction and Object Recognition
IEEE International Conference on Advances in Pattern Recognition (ICAPR), Indian Statistical Institute, Bangalore
Singh, J. & Bosilj, P. Align, Then Sample: Reference Region Alignment for Equivariance and Invariance
Manuscript under review
Chauhan, V. K., Wang, X., Singh, J., Zhou, J. & Agrawal, R. A Practical Tutorial on Agentic AI
Manuscript under review
Singh, S. P., Ranade, S. K. & Singh, J. WaveMaxCNN-Net: Hierarchical Attention for Skin Lesion Classification Using Discrete Wavelet Transform and Multi-Axis Vision Transformer-Based Convolutional Neural Networks
Manuscript under review
Arora, K., Harika, K. S., Singh, J. & Singh, K. MCHF-Net: Multiscale Features Fusion of Convolutional Neural Network and Handcrafted Descriptors for Apple (Annona squamosa) Leaf Disease Classification
Manuscript under review
Singh, H., Singh, J., Singh, S., Singh, R., Shahid, S. I., Hassan, M. & Tayarani Najaran, M. Exploring Student Perception on Gen AI Adoption in Higher Education: A Descriptive Study
arXiv:2603.27777
Singh, J., Bosilj, P. & Cielniak, G. Accurate Shift Invariant Convolutional Neural Networks Using Gaussian-Hermite Moments
arXiv:2603.17098
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