	Tu-Bao Ho (Hồ Tú Bảo) 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). Ho Lab: Machine Learning and Data Mining Japan Advanced Institute of Science and Technology (JAIST) JAIST office in Vietnam Knowledge Discovery Nuggets Professional Services In the steering committee of PAKDD (Pacific-Asia Conferences on Knowledge Discovery and Data Mining, co-chair), PRICAI (Pacific Rim International Conference on Artificial Intelligence), IEEE RIVF (IEEE Conference on Computing and Communication Technologies, chair), ACML (Asian Conference on Machine Learning ). In the program committee of various international conferences, including: PRICAI'90, PC Co-Chair of PRICAI 2008; ICML'05; ICML'06; PC Vice-Chair of ICDM'06, ICDM'09; PAKDD 2000, PAKDD 2005 (PC co-chair), PAKDD 2006, PAKDD 2007, PAKDD 2008 (Area Chair), PAKDD 2009 (PC co-chair), PAKDD 2010; DS 2005, DS 2006, DS 2007; ECML/PKDD 2005, 2007, 2010; IFSR'05; RIVF 2006, PC-Chair of RIVF 2007, PC Co-Chair of RIVF 2008, General Co-chair RIVF 2009, PC Chair RIVF 2010; AI Nectar Track at the 21st AAAI 2006; SIGKDD'08; CIKM'09; Tutorial Chair of ACML'09; Area Chair ECML/PKDD 2010; ICDM 2011, etc. Co-organizer of some 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'12)
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) Vài bài viết tiếng Việt

Tu-Bao Ho

(Hồ Tú Bảo)

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).

Ho Lab: Machine Learning and Data Mining

Japan Advanced Institute of Science and Technology (JAIST)

JAIST office in Vietnam

Knowledge Discovery Nuggets

Professional Services

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

In the program committee of various international conferences, including: PRICAI'90, PC Co-Chair of PRICAI 2008; ICML'05; ICML'06; PC Vice-Chair of ICDM'06, ICDM'09; PAKDD 2000, PAKDD 2005 (PC co-chair), PAKDD 2006, PAKDD 2007, PAKDD 2008 (Area Chair), PAKDD 2009 (PC co-chair), PAKDD 2010; DS 2005, DS 2006, DS 2007; ECML/PKDD 2005, 2007, 2010; IFSR'05; RIVF 2006, PC-Chair of RIVF 2007, PC Co-Chair of RIVF 2008, General Co-chair RIVF 2009, PC Chair RIVF 2010; AI Nectar Track at the 21st AAAI 2006; SIGKDD'08; CIKM'09; Tutorial Chair of ACML'09; Area Chair ECML/PKDD 2010; ICDM 2011, etc.

Co-organizer of some 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'12)

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.