| 2026 | CCGRID | Scaling Real-Time Traffic Analytics on Edge-Cloud Fabrics for City-Scale Camera Networks. | Akash Sharma, Pranjal Naman, Roopkatha Banerjee, Priyanshu Pansari, Sankalp Gawali, Mayank Arya, Sharath Chandra, Arun Josephraj, Rakshit Ramesh, Punit Rathore, Anirban Chakraborty, Raghu Krishnapuram, Vijay Kovvali, Yogesh Simmhan |
| 2026 | HPDC | ATLAS: Efficient Out-of-Core Inference for Billion-Scale Graph Neural Networks. | Pranjal Naman, Yogesh Simmhan |
| 2026 | ICDE | Billion-Scale Fintech Analytics: Scalable Data Management and Anomaly Detection at NPCI. | Bharadwaj Dasari, Turaga Sai Dhiraj, Ganesh Jambhrunkar, Thirumalai Kailasam, Charu Vikram, Saurav Singla, Pranjal Naman, Yogesh Simmhan |
| 2025 | HiPC | Towards Scalable Mining of Temporal Graph Motifs over Large-Scale Transaction Networks. | Hrishikesh Haritas, Abhinav Rawat, Pranjal Naman, Ganesh Jambhrunkar, Amit Khandelwal, Saurav Singla, Yogesh Simmhan |
| 2025 | HiPC | A GPU is All You Need: Rethinking Distributed and Out-of-Core GNN Training. | Pranjal Naman, Yogesh Simmhan |
| 2025 | ICDCS | Ripple: Scalable Incremental GNN Inferencing on Large Streaming Graphs. | Pranjal Naman, Yogesh Simmhan |
| 2024 | EuroPar | Optimizing Federated Learning Using Remote Embeddings for Graph Neural Networks. | Pranjal Naman, Yogesh Simmhan |
| 2024 | EuroPar | Topology-Aware Aggregation for Federated Graph Learning. | Pranjal Naman, Yogesh Simmhan |
| 2024 | HiPC | Performance Trade-offs in GNN Inference: An Early Study on Hardware and Sampling Configurations. | Pranjal Naman, Yogesh Simmhan |
| 2023 | CCGRID | To Think Like a Vertex (or Not) for Distributed Training of Graph Neural Networks. | Varad Kulkarni, Akarsh Chaturvedi, Pranjal Naman, Yogesh Simmhan |
| 2023 | CCGRID | Performance Modelling of Graph Neural Networks. | Pranjal Naman, Yogesh Simmhan |