Journal Publications

1. James, J., Shields, I., Yogarajan, V., Keegan, P. J., Watson, C. I., Jones, P. L., & Mahelona, K. (2023). The development of a labelled te reo Māori–English bilingual database for language technology. Language Resources and Evaluation, 1-26. 

2. Bensemann, J., Brown, J., Witbrock, M., & Yogarajan, V. (2023). Is it possible to preserve a language using only data? Cognitive Science, e13300. 

3. Yogarajan, V., Dobbie, G., Leitch, S., Keegan, T. T., Bensemann, J., Witbrock, M., Asrani, V., & Reith, D. (2022). Data and model bias in artificial intelligence for healthcare applications in New Zealand. Frontiers in Computer Science, 4, 1070493. 

4. Yogarajan, V., Pfahringer, B., & Mayo, M. (2020). A review of automatic end-to-end de-identification: Is high accuracy the only metric?, Applied Artificial Intelligence Journal, pp 1-19.

5. Yogarajan, V., Mayo, M., & Pfahringer, B. (2018). Protection for Health Information Research in New Zealand District Health Boards. The New Zealand Medical Journal, 131(1485), pp 19-26. (pdf)

Conference Publications

6. Keesing, A., Koh, Y.S., Yogarajan, V., & Witbrock, M. (2023). Emotion Recognition ToolKit (ERTK): Standardising Tools For Emotion Recognition Research, In Proceedings of the 31st ACM International Conference on Multimedia (MM '23). Association for Computing Machinery, USA, 9693–9696. (CORE ranking A*, Best Open-Source Award)

7. Yogarajan, V., Dobbie, G., & Gouk, H. (2023). Effectiveness of Debiasing Techniques: An Indigenous Qualitative Analysis. In ICLR TinyPapers.

8. Knowles, K., Bensemann, J., Prado, D. B., Yogarajan, V., Witbrock, M., Dobbie, G., & Chen, Y. (2023). Neuromodulation Gated Transformer. In ICLR TinyPapers.

9. Yogarajan, V., Dobbie, G., Leitch, S., & Reith, D. (2023). Developing a Fair AI-based Healthcare Framework with Feedback Loop. In KDH@IJCAI. 

10. Yogarajan, V., Dobbie, G., Pistotti, T., Bensemann, J., & Knowles, K. (2023). Challenges in Annotating Datasets to Quantify Bias in Under-represented Society. In EthAIcs@IJCAI.

11. Knowles, K., Witbrock, M., Dobbie, G. & Yogarajan, V. (2023) A Proposal for a Language Model Based Cognitive Architecture. Proceedings of the 2023 AAAI Fall Symposium on Integrating Cognitive Architectures and Generative Models. Arlington, VA, USA, AAAI Press. 

12. Chanajitt, R., Pfahringer, B., Gomes, H. M., & Yogarajan, V. (2022). Multiclass Malware Classification Using Either Static Opcodes or Dynamic API Calls. In AI 2022: Advances in Artificial Intelligence: 35th Australasian Joint Conference, AI 2022, Perth, WA, Australia, December 5–8, 2022, Proceedings (pp. 427-441). Cham: Springer International Publishing.

13. Trye, D., Yogarajan, V., König, J., Keegan, T. T., Bainbridge, D., & Apperley, M. (2022). A Hybrid Architecture for Labelling Bilingual Māori-English Tweets. In Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022 (pp. 119-130). (CORE ranking A)

14. Yogarajan, V., Pfahringer, B., Smith, T., & Montiel, J. (2022). Concatenating BioMed-Transformers to Tackle Long Medical Documents and to Improve the Prediction of Tail-end Labels. In International Conference on Artificial Neural Networks (pp. 209-221). Cham: Springer Nature Switzerland.

15. James, J., Yogarajan, V., Shields, I., Watson, C., Keegan, P., Mahelona, K. & Jones, P. (2022). Language Models for Code-switch Detection of te reo Māori and English in a Low-resource Setting. Findings of the Association for Computational Linguistics: NAACL 2022, pp 650-660. (CORE ranking A)

16. Yogarajan, V., Montiel, J., Smith, T., & Pfahringer, B. (2022). Predicting COVID-19 Patient Shielding: A Comprehensive Study. In: Long, G., Yu, X., Wang, S. (eds) Advances in Artificial Intelligence. AI 2022. Lecture Notes in Computer Science, vol 13151. Springer, Cham. (Best Video Presentation Award)

17. Yogarajan, V.,  Montiel J., Smith T., & Pfahringer B. (2021) Transformers for Multi-label Classification of Medical Text: An Empirical Comparison. In: Tucker A., Henriques Abreu P., Cardoso J., Pereira Rodrigues P., Riaño D. (eds) Artificial Intelligence in Medicine. AIME 2021. Lecture Notes in Computer Science, vol 12721. Springer, Cham. (CORE ranking A)

18. Yogarajan, V., Gouk, H., Smith, T., Mayo, M., & Pfahringer, B. (2020). Comparing High Dimensional Word Embeddings Trained on Medical Text to Bag-of-Words For Predicting Medical Codes. Proceedings of the Asian Conference on Intelligent Information and Database Systems (ACIIDS 2020). Lecture Notes on Artificial Intelligence (LNAI), Springer.

19. Mayo, M., & Yogarajan, V. (2019). A nearest neighbour-based analysis to unmask patients from continuous glucose monitor data. Proceedings of the Asian Conference on Intelligent Information and Database Systems (ACIIDS 2019). Lecture Notes on Artificial Intelligence (LNAI), Springer, 349-360. (Best Paper Award)

20. Yogarajan, V., & Ragupathy, R. (2019). Adoption of International Privacy Standards in New Zealand Health Information Research. The New Zealand Medical Journal, 132 (1492), 70-72.

21. Ragupathy, R., Yogarajan, V., & Luoni, C. (2019). Health Information Research Privacy Standards Should Include Māori Perspectives on Privacy. The New Zealand Medical Journal, 132 (1494), 64-67.

22. Yogarajan, V., & Ragupathy, R. (2019). Research using electronic health records: not all de-identified datasets are created equal. The Journal of Primary Health Care.

23. Ragupathy, R., & Yogarajan, V. (2018). Applying the Reason Model to enhance health record research in the age of  'big data'. The New Zealand Medical Journal, 131(1478), 65-67.

Theses

24. Yogarajan V. (2022). Domain-specific language models for multi-label classification of medical text. (PhD Thesis, University of Waikato).

25. Yogarajan, V. (2018). Automatic de-identification of electronic health records using word embeddings. (Masters Thesis, University of Waikato).

26. Shanmuganathan, V. (2007). The Phase error of Explicit Runge-Kutta-Nystrom Methods. (Masters Thesis, University of Auckland, maiden name)

Poster 

Yogarajan, V., Leitch, S., Reith, D., & Witbrock, M. (2022). Machine Learning to Analyse and Reduce Healthcare Harm in New Zealand General Practice. HealthTech Conference-22.

Yogarajan, V., Smith, T., Mayo, M. & Pfahringer, B. (2019). Comparing word embeddings to bag-of-words for predicting medical codes. MLSS 2019 London. (pdf)

Invited Guest Talks 

Yogarajan V. (2023). Chatting with ChatGPT and their friends. Guest Presenter at The Australian and New Zealand Society of Occupational Medicine Conference.

Yogarajan V. (2022). Artificial Intelligence to Improve Healthcare Outcomes of Underrepresented and Indigenous Populations. NAOI Health Intelligence event. 

Yogarajan V. (2022). AI: Benefits and Risks. Nottingham University (Hybrid)