| Asst Prof Manoranjan Dash
Assistant Professor
Division of Software & Information Systems
School of Computer Engineering
College of Engineering
Email: ASMDASH@ntu.edu.sg
Phone: (+65)6790 6167
Office: N4-02c-85 |
| Education |
- PhD National University of Singapore 2002
- MSc National University of Singapore 1998
- BScEngg Sambalpur University 1990
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| Biography |
| Asst Prof Manoranjan Dash is currently in the School of Computer Engineering since 2003. He received his Bachelor degree in Computer Science from National Institute of India (Rourkela), Master and Ph.D. degrees from the National University of SIngapore respectively. His research interests include data mining, machine learning, and their applications. He has done significant research work in his research areas and published over 40 top quality international conference and journal papers. He has been often invited as a program committee member or referee and reviewer for a number of premier conferences and journals, including SIGKDD, AAAI, SIAM SDM, PAKDD, DASFAA, TKDE, BMC Bioinformatics, VLDB Journal, etc. Dr. Dash is a member of ACM and IEEE. |
| Research Interests |
Data Mining
Machine Learning
Applications of Data Mining and MAchine Learning in Bioinformatics, Image Processing
Parallel Computing |
| Research Grant |
- NTU-Rolls-Royce Fuel Cell Systems Pte Ltd (RRFCS) Research Collaboration (2009-)
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| Current Projects |
- Improved Design via Evoltionary Algorithms
| Selected Publications | - M. Dash
W. Ng. (2009). Outlier detection in transactional data. Intelligent Data Analysis, .
- W. Ng, M. Dash. (2009). A comparison between approximate counting and sampling methods for frequent pattern mining on data streams. Intelligent Data Analysis, .
- Manoranjan Dash, Aayush Singhania. (2008). Mining in Large Noisy Domain. ACM Journal of Data and Information Quality, .
- Z. Zhu, Y. S. Ong, M. Dash. (2007). Markov Blanket-Embedded Genetic Algorithm for Gene Selection. Pattern Recognition, 49(11), 13.
- M.Dash, P. Sceuermann*, S. Petrutiu*. (2006). pPOP: Efficient Yet Accurate Parallel Hierarchical Clustering using Partitioning. Data & Knowledge Engineering, .
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