Gaurav Singh

Varsity Lakes, Australia · Full Australian work rights · hi@gvsh.cc · ORCID · GitHub · Google Scholar · LinkedIn · PDF

Professional profile

Software developer and applied machine-learning researcher with current commercial experience building payment-platform software. I work across C#, .NET, TypeScript, Vue, REST APIs, Python, and modern machine-learning frameworks. My background spans computer science, engineering research, design, and a decade of university teaching. I have first-author peer-reviewed publications in applied machine learning and visual-mathematical literacy, and a Master of Information Technology focused on generative models for environmental time-series data.

Technical skills

Software engineering: C#, .NET, JavaScript, TypeScript, Vue, Azure DevOps, REST APIs, Git, and CI/CD; Docker and AWS.

Machine learning: supervised learning, feature engineering, model evaluation and cross-validation, time-series forecasting; scikit-learn, PyTorch, TensorFlow/Keras.

Deep learning: neural networks for computer vision, sequence modelling, and generative modelling.

Data and modelling: Python, NumPy, Pandas, SQL, Matplotlib; probability and statistical inference, exploratory data analysis, uncertainty quantification, nonlinear optimisation, techno-economic modelling, and longitudinal modelling.

LLMs and agents: LLM API integration, structured document extraction, agentic workflows, and evaluation.

Research interests

How representation choices shape what models can learn; making models work where data is sparse and incomplete; mathematical modelling of physical and economic systems; visual-mathematical literacy.

Education

Experience

Publications

A Framework for Visual-Mathematical Literacy in Applied Machine Learning: Why Representation Choices Shape What Models Can Learn. G. Singh, R. S. Dhari. IOP Machine Learning, 2026. doi:10.1088/3049-4761/ae7df3
Techno-economic pathways modeling and nonlinear optimized SEEA-ROI longitudinal dynamic simulation for decarbonising Australian heavy transportation systems. G. Singh, E. Chang, Y. Karaca. Fractals, 2026. doi:10.1142/S0218348X26400633
Automated detection of deformation mechanisms in re-entrant honeycomb auxetics using machine learning. G. Singh, R. S. Dhari, Z. Javanbakht. International Journal of Protective Structures, 2024. doi:10.1177/20414196241281069
ReRide: A Bike Area Network for Embodied Self-monitoring during Motorbike Commute. N. Bagalkot, G. Singh, V. Rath, T. Sokoler, A. Shukla. Proceedings of the Thirteenth International Conference on Tangible, Embedded, and Embodied Interaction (TEI '19), 443-450, 2019. doi:10.1145/3294109.3300986
ReRide. N. Bagalkot, T. Sokoler, R. Shaikh, G. Singh, A. E. Lillie, P. Dixit, A. Rai, V. Chakravarthy, A. Senthil. Human-Computer Interaction - INTERACT 2017. Lecture Notes in Computer Science, vol 10516. Springer, Cham, 2017. doi:10.1007/978-3-319-68059-0_43
SnapTag: Leveraging Situated Memory to enhance self-efficacy for well-being. S. Baadkar, G. Singh, A. Saraf, N. Bagalkot. Proceedings of the India HCI 2014 Conference on Human-Computer Interaction, 136-141, 2014. doi:10.1145/2676702.2676719

Selected software

Iterflow: Composable Streaming Statistics for JavaScript. G. Singh. Mathscapes, 2026. doi:10.5281/zenodo.18610143

Teaching and academic leadership

Designed and taught 30 courses and workshops over a decade (2013–2022), mostly at Srishti Manipal Institute in Bangalore, spanning creative coding, algorithmic botany, physical computing, machine learning, interaction design, and human-computer interaction.

Awards

Service

Supervision