Isuru Wijesiri

Colombo, Sri Lanka

Interdisciplinary ML researcher and engineer spanning NLP, agentic AI, privacy-preserving graph ML, and computer vision. I build end-to-end AI systems that ship to production and also publish research on scalable learning systems.

At WSO2 (AI R&D), I lead applied NLP and intelligent automation for enterprise integration, most notably natural-language to Synapse DSL / Ballerina code generation with version-aware retrieval and execution-guided verification. The deployed system (MI Copilot) currently serves 300+ active daily users.

My earlier research includes a memory-efficient federated GCN for distributed link prediction that enables training on billion-edge graphs on commodity hardware (IEEE Big Data 2020), and a self-supervised multimodal anomaly detection approach for autonomous drones (IEEE Signal Processing Cup 2020 – runner-up, arXiv preprint).

Most recently, I joined a Google-funded project as an External Research Collaborator with the University of Moratuwa, leading research on Automatic Post-Editing (APE) for low-resource languages (Sinhala, Tamil).

Interests: NLP, Agentic AI, Retrieval-augmented generation, Code/DSL synthesis, Federated Learning, Privacy-preserving ML, Graph ML

news

Jul 31, 2025	Started as Lead Research Collaborator at University of Moratuwa on Google-funded research on Automatic Post-Editing for low-resource languages (Sinhala, Tamil).
Feb 28, 2025	Presented a technical deep-dive on blockchain architecture, consensus algorithms, and distributed ledger mechanisms at WSO2 Technology Conference 2025. Watch the talk.
Jan 14, 2025	Released lightweight code retrieval models (22M/33M parameters) on Hugging Face: L6 and L12 , achieving 97% Recall@10 for domain-specific code search.

latest posts

Mar 18, 2026	Building a Claude Code-Style AI Agent for Enterprise Integration

selected publications

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.