publications

Research publications by Isuru Wijesiri on NLP, federated graph learning, anomaly detection, and agentic AI systems.

2020

Memory-Efficient Graph Convolutional Network-Based Distributed Link Prediction

Isuru Wijesiri and others

In 2020 IEEE International Conference on Big Data (Big Data), 2020

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Introduces a memory-efficient, federated training scheme that partitions large graphs across JasmineGraph and runs parallel GCN workers. This lets a single commodity server train link-prediction models on datasets as big as DBLP-V11 (10+ GB) that conventional GCNs cannot load, and cuts training time by 3-5x on Reddit and Twitter graphs compared with standard, in-memory methods.
Anomaly Detection using Deep Reconstruction and Forecasting for Autonomous Systems

Isuru Wijesiri and others

arXiv preprint arXiv:2006.14556, 2020

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Self-supervised anomaly-detection system for autonomous drones: forecasted next video frame with a conditional GAN and IMU vectors with dual LSTM (autoencoder + forecaster); achieved 94% accuracy / 0.95 F1 on video, 100% accuracy / 0.98 F1 on IMU, earning Runner-up, IEEE Signal Processing Cup 2020.