	Tu-Bao Ho Professor, School of Knowledge Science email: bao@jaist.ac.jp phone&fax:(81)-761-51-1730	Education and Career B.Tech. degree in Applied Mathematics from Hanoi University of Technology (1978), M.S. and Ph.D. degrees in Computer Science from Pierre and Marie Curie University, Paris (1984, 1987), and Habilitation from Paris Dauphine University (1998). Ph.D. candidate (1983-1987) at INRIA (the French National Institute for Research in Computer Science and Control, France), visiting fellow (1992) at Wisconsin-Madison University (USA). Researcher (since 1979) and Associate Professor (1991) at the Institute of Information Technology, Vietnam Academy of Science and Technology (VAST), Visiting Associate Professor (1993-1997) at the School of Information Science, and Professor (1998) at the School of Knowledge Science (JAIST). Knowledge Creating Methodology Laboratory Japan Advanced Institute of Science and Technology (JAIST) Knowledge Discovery Nuggets Professional Services Member of the Steering Committee of PAKDD (Pacific-Asia Conferences on Knowledge Discovery and Data Mining), PRICAI (Pacific Rim International Conference on Artificial Intelligence), IEEE RIVF (IEEE Conference on Computing and Communication Technologies ), and ACML (Asian Conference on Machine Learning ). Member of Program Committee of International Conferences: PRICAI'90, PC Co-Chair of PRICAI 2008; ICML'05; ICML'06; PC Vice-Chair of IEEE ICDM'06, IEEE ICDM'09; DaWak'02, DaWak'03, DaWak'04, DaWak'05 (DEXA); IAT'03; ISMIS 2003; PAKDD 2000, PAKDD 2005 (PC co-chair), PAKDD 2006, PAKDD 2007, PAKDD 2008 (Area Chair), PAKDD 2009 (PC co-chair); DS 2005, DS 2006, DS 2007; AWIC 2004; ECML/PKDD 2005, ECML/PKDD 2007; IFSR'05; RIVF 2006, PC-Chair of RIVF 2007, PC Co-Chair of RIVF 2008, General Co-chair RIVF 2009; AI Nectar Track at the 21st AAAI 2006; SIGKDD'08; CIKM'09; ICDM'09; Tutorial Chair of ACML'09;etc. Some recent conferences: 9th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD'05) 10th Pacific Rim International Conference on Artificial Intelligence (PRICAI'08) IEEE-RIVF International Conference on Computing and Computation Technology (IEEE-RIVF'09) First Asian Conference on Machine Learning (ACML'09)
Research Interests Knowledge-Based Systems A new trend in knowledge-based systems technology is that expertise transfer paradigms have been replaced in recent years by knowledge modelling paradigms. To this end we are interested in conceptual modelling processes and automatic knowledge acquisition methods for knowledge-based decision making models. Machine Learning Machine learning is the study of computer algorithms that improve automatically through experience. We currently are interested in several 'tough' topics such as learning imbalanced data, learning mixed and heterogeneous data, learning with kernels, etc. Knowledge Discovery and Data Mining This rapidly growing interdisciplinary field merges databases, statistics, machine learning and others in order to extract useful knowledge from data. We are mainly interested in text mining and scientific data mining. For the former, we are working on text-based scientific knowledge creation methods with focus on information extraction and emerging trend detection. For the latter, we are working on several mining problems in medicine, biology and materials science such as hepatitis study, protein-protein interactions, crystal structure determination, water properties in biological systems, etc. Publications by years Publications since Jaist (1994) Some writings in Vietnamese

Tu-Bao Ho

Professor, School of Knowledge Science

email: bao@jaist.ac.jp phone&fax:(81)-761-51-1730

Education and Career

B.Tech. degree in Applied Mathematics from Hanoi University of Technology (1978), M.S. and Ph.D. degrees in Computer Science from Pierre and Marie Curie University, Paris (1984, 1987), and Habilitation from Paris Dauphine University (1998). Ph.D. candidate (1983-1987) at INRIA (the French National Institute for Research in Computer Science and Control, France), visiting fellow (1992) at Wisconsin-Madison University (USA).

Researcher (since 1979) and Associate Professor (1991) at the Institute of Information Technology, Vietnam Academy of Science and Technology (VAST), Visiting Associate Professor (1993-1997) at the School of Information Science, and Professor (1998) at the School of Knowledge Science (JAIST).

Knowledge Creating Methodology Laboratory

Japan Advanced Institute of Science and Technology (JAIST)

Knowledge Discovery Nuggets

Professional Services

Member of the Steering Committee of PAKDD (Pacific-Asia Conferences on Knowledge Discovery and Data Mining), PRICAI (Pacific Rim International Conference on Artificial Intelligence), IEEE RIVF (IEEE Conference on Computing and Communication Technologies ), and ACML (Asian Conference on Machine Learning ).

Member of Program Committee of International Conferences: PRICAI'90, PC Co-Chair of PRICAI 2008; ICML'05; ICML'06; PC Vice-Chair of IEEE ICDM'06, IEEE ICDM'09; DaWak'02, DaWak'03, DaWak'04, DaWak'05 (DEXA); IAT'03; ISMIS 2003; PAKDD 2000, PAKDD 2005 (PC co-chair), PAKDD 2006, PAKDD 2007, PAKDD 2008 (Area Chair), PAKDD 2009 (PC co-chair); DS 2005, DS 2006, DS 2007; AWIC 2004; ECML/PKDD 2005, ECML/PKDD 2007; IFSR'05; RIVF 2006, PC-Chair of RIVF 2007, PC Co-Chair of RIVF 2008, General Co-chair RIVF 2009; AI Nectar Track at the 21st AAAI 2006; SIGKDD'08; CIKM'09; ICDM'09; Tutorial Chair of ACML'09;etc.

Some recent conferences:

9th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD'05)

10th Pacific Rim International Conference on Artificial Intelligence (PRICAI'08)

IEEE-RIVF International Conference on Computing and Computation Technology (IEEE-RIVF'09)

First Asian Conference on Machine Learning (ACML'09)

Research Interests

Knowledge-Based Systems

A new trend in knowledge-based systems technology is that expertise transfer paradigms have been replaced in recent years by knowledge modelling paradigms. To this end we are interested in conceptual modelling processes and automatic knowledge acquisition methods for knowledge-based decision making models.

Machine Learning

Machine learning is the study of computer algorithms that improve automatically through experience. We currently are interested in several 'tough' topics such as learning imbalanced data, learning mixed and heterogeneous data, learning with kernels, etc.

Knowledge Discovery and Data Mining

This rapidly growing interdisciplinary field merges databases, statistics, machine learning and others in order to extract useful knowledge from data. We are mainly interested in text mining and scientific data mining. For the former, we are working on text-based scientific knowledge creation methods with focus on information extraction and emerging trend detection. For the latter, we are working on several mining problems in medicine, biology and materials science such as hepatitis study, protein-protein interactions, crystal structure determination, water properties in biological systems, etc.