Bsc Eng. (Hons) (SLIIT)
Velayuthan Menan Graduated from Sri Lanka Institute of Information Technology, after his graduation he worked as an Senior Associate at Stax LLC in the Data Science and Analytics team. He mainly focused on RnD on NLP tools during his time in Stax. Later with his interest in research, he joined the SLIIT Computer Faculty to contribute to a World Bank funded project as part of the AHEAD program, here he focused his research in building mobile friendly light weight head pose estimation models using Deep Set architecture. He then got an opportunity to work as an AI research fellow at Pi School, Rome Italy. Currently he is working as an AI Research Engineer at Department of Computer Science & Engineering, University of Moratuwa, Sri Lanka for a Google funded project on Neural Machine Translation.
Keywords: Natural Language Processing | Machine Learning / Deep Learning | Sinhala |
Theses: MSc Major Component Research
Menan Velayuthan, "Multi-Domain Neural Machine Translation with Knowledge Distillation For Low Resource Languages", University of Moratuwa, 2025
Conference Papers
Aloka Fernando, Nisansa de Silva, Menan Velayuthan, Charitha Rathnayaka, and Surangika Ranathunga, "Improving the quality of Web-mined Parallel Corpora of Low-Resource Languages using Debiasing Heuristics", in Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 2025, pp. 28252--28269. doi: 10.18653/v1/2025.emnlp-main.1435
Surangika Ranathunga, Nisansa de Silva, Menan Velayuthan, Aloka Fernando, and Charitha Rathnayake, "Quality Does Matter: A Detailed Look at the Quality and Utility of Web-Mined Parallel Corpora", in Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), St. Julian{'}s, Malta: Association for Computational Linguistics, mar. 2024, pp. 860--880. 
Workshop Papers
Menan Velayuthan, Nisansa De Silva, and Surangika Ranathunga, "Encoder-Aware Sequence-Level Knowledge Distillation for Low-Resource Neural Machine Translation", in Proceedings of the Eighth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2025), 2025, pp. 161--170.
Menan Velayuthan, Dilith Jayakody, Nisansa de Silva, Aloka Fernando, and Surangika Ranathunga, "Back to the Stats: Rescuing Low Resource Neural Machine Translation with Statistical Methods", in Proceedings of the Ninth Conference on Machine Translation, 2024, pp. 901--907. doi: 10.18653/v1/2024.wmt-1.87
PI: Surangika Ranathunga
We propose to create a multi-domain Neural Machine Translation (NMT) System for Sinhala, Tamil, and English, the official languages of Sri Lanka.
PI: Nisansa de Silva
We propose to advance natural language processing for low-resource settings by leveraging data augmentation, transfer learning, and model adaptation techniques to overcome the scarcity of annotated data and linguistic resources.
PI: Nisansa de Silva
We propose to build and analyze Sinhala language resources, datasets, and models to strengthen NLP research for this low-resource language, while connecting it to English and Tamil through bilingual lexicons, embeddings, and cross-lingual methods.
