Biography
Dr Savitha Ramasamy is an Associate Professor at Singapore University of Technology and Design (SUTD). Prior to this, she was a Principal Scientist and Research Group Leader at A*STAR Institute for Infocomm Research. Her research focuses on the development of robust, transparent, and trustworthy AI systems for real-world deployment.
Her work spans the full spectrum of Technology Readiness Levels (TRLs), integrating foundational advances in artificial intelligence and machine learning with applied research in operational environments. She is particularly interested in bridging the gap between theoretical AI and its adoption in engineering domains where system reliability and resilience are critical.
Dr Ramasamy collaborates extensively with industry partners across aviation, maintenance, repair and overhaul (MRO), maritime, and manufacturing sectors. Through these collaborations, she has developed AI-driven solutions for the management of complex engineering assets, including aircraft, vessels, and built infrastructure. She led a decade-long, multi-phase research initiative with Singapore Airlines focused on AI for improving airline operations. In the maritime domain, her work explores AI-enabled approaches for enhancing sustainability and operational performance. Her broader research interests include robust and explainable AI, as well as human-inspired machine learning. She is also actively engaged in translating research into deployable systems through strategic collaborations, resulting in a portfolio of intellectual property.
Dr Ramasamy received her Ph.D. from Nanyang Technological University in 2011, where she developed novel algorithms for complex-valued signal processing, including online learning in complex-valued neural networks. She subsequently held a postdoctoral position at NTU, working on AI models inspired by principles of human learning.
She has authored over 200 research publications in leading AI conferences, including AAAI, NeurIPS, ICLR, ACL, EMNLP, and MICCAI, as well as top-tier journals such as IEEE Transactions on Neural Networks and Learning Systems, IEEE Journal of Biomedical and Health Informatics, and Elsevier Neural Networks. She holds two patents, with additional filings in progress. Her work has been recognized with several accolades, including the Prestigious Engineering Achievement Award (2024) from the Institution of Engineers Singapore and the Firefly Award for Most Innovative Project (Silver, 2025) from the Ministry of Trade and Industry. Her team was also shortlisted for the President’s Technology Award (2025). She is a Senior Member of IEEE (2018) and was named among the 100 Women in GovTech (2024) and the inaugural 100 Women in Technology (2020).
Selected recent publications
- Gautam, C., Hean, L. C., Das, A., Li, X., & Ramasamy, S., “TGCD: A Framework for Generalized Category Discovery in Time-Series Data,” Proceedings of the AAAI Conference on Artificial Intelligence, 2026.
- Han, J., Ramasamy, S., Chong, A., “Data-efficient fault detection and prognosis for predictive maintenance of building systems using a hybrid Dual-Calibrated Particle Filter,” Energy and Buildings, 2026.
- Guo, L., Xu, I. Q., Nag, S., Xu, J., Chai, J., Simmons, Z., Ramasamy, S., Yeo, C. J. J., “Predicting Amyotrophic Lateral Sclerosis Mortality With Machine Learning in Diverse Patient Databases,” Muscle & Nerve, 2025.
- Das, A., Gautam, C., Agrawal, P., Yang, F., Liu, Y., Ramasamy, S., “MedGCD: Generalized Category Discovery in Medical Imaging,” MICCAI, 2025.
- Cao, Z., Poh, J. W. J., Guo, Y., Gautam, C., Nambiar, M., Chia, S. Y., “Towards Reliable Prediction: A Bayesian Continual Learning Approach for Clinical Time-series Data,” IEEE Journal of Biomedical and Health Informatics, 2025.
- Meunier, R., Benamara, F., Moriceau, V., Qiao, Z., Ramasamy, S., “CrisisTS: Coupling Social Media Textual Data and Meteorological Time Series for Urgency Classification,” ACL, 2025.
- Qiao, Z., Liu, C., Zhang, Y., Jin, M., Pham, Q., Wen, Q., Suganthan, P. N., Jiang, X., Ramasamy, S., “Multi-Scale Fine-Tuning for Encoder-based Time Series Foundation Models,” NeurIPS, 2025.
- Parameswaran, S., Fang, Y., Gautam, C., Ramasamy, S., Li, X., “Learning to Identify Seen, Unseen and Unknown in the Open World,” WACV, 2025.
- Ma’sum, M. A., Pratama, M., Ramasamy, S., Liu, L., Habibullah, H., Kowalczyk, R., “PROL: Rehearsal-Free Continual Learning in Streaming Data via Prompt Online Learning,” ICCV, 2025.
- Ma’sum, M. A., Pratama, M., Ramasamy, S., Liu, L., Habibullah, H., Kowalczyk, R., “Vision and Language Synergy for Rehearsal-Free Continual Learning,” ICLR, 2025.
For a full list of publications, please visit: https://scholar.google.com/citations?hl=en&user=SLQ1lxgAAAAJ