Research & Innovation.

Introducing the Cognitive Learning AI Framework (CLAF): Evaluation dimensions for educational AI as infrastructure

Singh, R., Cotto, J., Yadav, V., & Singh, A. (2026). Introducing the Cognitive Learning AI Framework (CLAF): Evaluation dimensions for educational AI as infrastructure. In Proceedings of AIR-RES 2026.

Provenance-aware hybrid NLP–LLM pipelines for educational content generation

Singh, R., Cotto, J., Yadav, V., & Singh, A. (2026). Provenance-aware hybrid NLP–LLM pipelines for educational content generation. In Proceedings of AIR-RES 2026.

From infrastructure to impact: Measuring learning outcomes in provenance-aware hybrid NLP–LLM study aids

Singh, R., Cotto, J., Yadav, V., & Singh, A. (2026). From infrastructure to impact: Measuring learning outcomes in provenance-aware hybrid NLP–LLM study aids. In Proceedings of AIR-RES 2026.

Hybrid NLP–LLM pipelines for automatic learning content generation

Singh, R., & Cotto, J. (2025). Hybrid NLP–LLM pipelines for automatic learning content generation. In Proceedings of LLM2025.

Mesh of meshes: Event-native convergence of service, data, and agent architectures for AI-ready enterprises

Sigurjonsson, T., & Singh, R. (2026). Mesh of meshes: Event-native convergence of service, data, and agent architectures for AI-ready enterprises. In Proceedings of the International Conference on Software Engineering and Data Engineering (CSCE 2026). Springer Lecture Notes in Networks and Systems.

A symbolic-semantic framework for public health surveillance

Yadav, V. S., & Singh, R., Singh, A. (2026). A symbolic-semantic framework for public health surveillance. In Proceedings of the International Conference on Software Engineering and Data Engineering (CSCE 2026). Springer Lecture Notes in Networks and Systems.

From data lakes to context lakes: Probabilistic knowledge graphs for autonomous agent memory

Singh, A., Singh, R., & Yadav, V. (2026). From data lakes to context lakes: Probabilistic knowledge graphs for autonomous agent memory. In Proceedings of the International Conference on Software Engineering and Data Engineering (CSCE 2026). Springer Lecture Notes in Networks and Systems.

Machine learning workflow for laboratory variability prediction

Bytyqi, F., & Singh, R. (2026). Machine learning workflow for laboratory variability prediction. In Proceedings of the International Conference on Software Engineering and Data Engineering (CSCE 2026). Springer Lecture Notes in Networks and Systems.

Evaluating TOGAF and E2A frameworks: A quantitative study of enterprise architecture effectiveness

Pacheco, F. L., & Singh, R. (2026). Evaluating TOGAF and E2A frameworks: A quantitative study of enterprise architecture effectiveness. In Proceedings of the International Conference on Information Systems and Technologies (CISTI 2026).

Modeling hierarchical data in SQL databases: Introduction to an efficient development framework

Singh, R., & Clement, K. (2013). Modeling hierarchical data in SQL databases: Introduction to an efficient development framework. In Proceedings of the Software Engineering and Data Engineering Conference (SEDE 2013), Los Angeles, CA.

Algorithms for discovering potentially interesting patterns

Singh, R., Johnsten, T., Raghavan, V. V., & Xie, Y. (2011). Algorithms for discovering potentially interesting patterns. International Journal of Granular Computing, Rough Sets and Intelligent Systems, 2(2), 107–122.

An efficient approach for discovering interesting patterns from biomedical data

Singh, R., & Yadav, V. (2011). An efficient approach for discovering interesting patterns from biomedical data. In Proceedings of the International Conference on Bioinformatics and Computational Biology (BICoB 2011), New Orleans, LA.

Efficient algorithm for discovering potential interesting patterns with closed itemsets

Singh, R., Johnsten, T., Raghavan, V. V., & Xie, Y. (2010). Efficient algorithm for discovering potential interesting patterns with closed itemsets. In Proceedings of the IEEE International Conference on Granular Computing (GrC 2010), San Jose, CA.

An efficient algorithm for discovering positive and negative patterns

Singh, R., Johnsten, T., Raghavan, V. V., & Xie, Y. (2009). An efficient algorithm for discovering positive and negative patterns. In Proceedings of the IEEE International Conference on Granular Computing (GrC 2009), Nanchang, China.