(Hồ Tú Bảo)
Professor, School of Knowledge Science
email: email@example.com 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 à diriger des recherches 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).
In the steering committee of PAKDD (Pacific-Asia Conferences on Knowledge Discovery and Data Mining, Chair), ACML (Asian Conference on Machine Learning, Co-Chair ), IEEE RIVF (IEEE Conference on Research, Innovation, and Vision for the Future), PRICAI (Pacific Rim International Conference on Artificial Intelligence, SC member).
In the program committee of various international conferences, including:
PC Co-Chair of PRICAI 2008;
PC Vice-Chair of ICDM'06,
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;
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;
Tutorial Chair of ACML'09;
Area Chair ECML/PKDD 2010;
PAKDD 2018, etc.
Co-organizer of some recent conferences:
IEEE-RIVF Research, Innovation, and Vision for the Future (IEEE-RIVF 2016)
6th Asian Conference on Machine Learning (ACML2014)
19th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2015)
22nd Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2018)
Machine learning is the study of computer algorithms that improve automatically through experience. It is one of the most exciting areas in computer science that have witnessed tremendous developments in the past few decades. Our work ranges from basic research on various machine learning paradigms to their applications.
This rapidly growing interdisciplinary field merges statistics (and other related mathematics disciplines), machine learning (and other advances in computer science) and domain knowledge to manage and analyze data for supporting human making right decisions in complex circumstances. We are interested in developing statistical learning methods to deal with large and complex data and applying data science methods in real world problems in agriculture, transportation, smart city, etc. and in particular e-health with exploiting electronic medical records.
Computational science is science about using math and computing to do research in other sciences. We work on modelling, simulation and computation methods using high performance computers. We are interested in problems in computational biomedicine, computational linguistics, and computationa environment, among others.