Research & innovation
Research that earns its way into products.
We investigate methods at the edge of AI — and only carry forward what holds up under rigorous evaluation.
Focus areas
What we work on
AI & deep learning research
Model architectures, training methods and rigorous evaluation for real-world data.
Medical imaging applications
Learning from MRI, fMRI, DTI and MR spectroscopy data to support neurological research.
Brain connectomics
Graph neural networks on structural and functional brain networks for disorder classification.
Scientific machine learning
Combining domain knowledge with data-driven models for reliable scientific inference.
Physics-informed neural networks
Embedding physical constraints into neural networks, including MR spectroscopy quantification.
Emerging technology research
Exploring quantum machine learning and other frontier technologies with a critical eye.
Peer-reviewed work
Founder publications
Our research foundation comes from the peer-reviewed work of our Founder & CEO, Udayakumar Palanivelu, on graph neural networks and deep learning for brain connectome analysis.
View Google Scholar profile (opens in a new tab)CST-GCNN: Spatio-Temporal Connectome Graph Network for Schizophrenia Classification
IEEE Access
DOI: 10.1109/ACCESS.2026.3586749 (opens in a new tab)Journal article · 2026
Connectome Graph Attention Network (CGAT): A multimodal approach for enhanced classification of Alzheimer's disease controls
Results in Engineering
DOI: 10.1016/j.rineng.2025.105200 (opens in a new tab)Journal article · 2025
Integrating deep learning in brain connectome mapping: Insights from a systematic review
International Research Journal of Multidisciplinary Scope (IRJMS), 6(2)
DOI: 10.47857/irjms.2025.v06i02.01953 (opens in a new tab)Systematic review · 2025
Connectome based schizophrenia prediction using structural connectivity — Deep Graph Neural Network (sc-DGNN)
Journal of X-Ray Science and Technology
DOI: 10.3233/XST-230426 (opens in a new tab)Journal article · 2024
fMRI data to connectivity matrices: Building functional connectomes for deep learning models
IEEE WIECON-ECE 2024
Conference paper · 2024
Diffusion MRI preprocessing for deep learning analysis of brain structural connectivity
12th International Conference on Advanced Computing (ICoAC), IEEE
Conference paper · 2023
Research collaboration
We welcome collaboration with universities, hospitals and research groups on AI for neuroimaging, scientific machine learning and research software.
Have a problem worth solving?
Tell us what you are working on. We will help you decide whether AI, software or research engineering is the right tool — and how to approach it.