Medical Computing Lab

Our research aims to develop novel computational and decision support techniques and frameworks that are simple, effective, and useful, both in general and in biomedicine.

Our team includes academics, researchers, clinicians, and students at the National University of Singapore, with collaborators from local and overseas institutions and hospitals.

Our technologies focus on modeling, reasoning, learning, and decision making in changing, uncertain, and resource-limited environments.

Our projects range from basic research on fundamental methodologies to application-driven translational work with demonstrated practical promise.

Our efforts contribute towards establishing Singapore as a regional center for advanced data analytics and outcomes research.

In biomedicine and health care, our  work facilitates cost-effective diagnostic, therapeutic, and prognostic management, policy and technology evaluation, and personalized medicine in various chronic and critical care domains.

In general, our work supports predictive modeling, outcome analysis, risk management, decision analysis, scenario planning, and adaptive computing in a wide range of decision tasks.

Recruitment

(Open – June 7, 2011) Medcomp is hiring, again! We are looking for a Post-Doctoral Research Fellow to join us. Click  HERE for more information.

Our recent publications

• Joshi, R. and T.-Y. Leong (2010). Context sensitive networks: A probabilistic context language for adaptive reasoning. (Submitted for publication)

• Guo, W. and T.-Y. Leong (2010). An analytic characterization of model minimization in factored Markov decision processes. In Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-2010), Atlanta, Georgia, AAAI Press. To appear.

• Chen, Q. and T.-Y. Leong (2010). Towards a multi-level game model for influenza epidemics. In Proceedings of the 2010 World Congress on Medical Informatics (MEDINFO 2010), Cape Town, South Africa, IOS Press. To appear.

• Yin, H. and T.-Y. Leong (2010). A model-driven approach to imbalanced data sampling in medical decision making. In Proceedings of the 2010 World Congress on Medical Informatics (MEDINFO 2010), Cape Town, South Africa, IOS Press. To appear.

• Li, G. and T.-Y. Leong (2009). Active learning for causal bayesian network structure with non‑symmetrical entropy. In T. Theeramunkong, B. Kijsirikul, N. Cercone and T. B. Ho, Eds. LNAI 5467 Advances in Knowledge Discovery and Data Mining: 13th Pacific-Asia Conference, PAKDD 2009 Proceedings, Bangkok, Thailand, Berlin / Heidelberg: Springer. vol. 5476/2009.

• Nguyen, D. T. H. and T.-Y. Leong (2009). A surprise triggered adaptive and reactive (STAR) framework for online adaptation in non-stationary environments. In C. J. Darken and G. M. Youngblood, Eds. Proceedings of the Fifth Artificial Intelligence and Interactive Digital Entertainment International Conference (AIIDE 2009), Palo Alto, California, AAAI Press: 82-87.

• Gong, T., C. L. Tan, T. Y. Leong, C. K. Lee, B. C. Pang, C. C. T. Lim, Q. Tian, S. Tang and Z. Zhang (2008). Text mining in radiology reports. In Proceedings of the 2008 Eighth IEEE International Conference on Data Mining (ICDM), Pisa, Italy, IEEE Computer Society: 815-820.