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WORLDCOMP'10 Invited Lecture - Prof. Vladmir Cherkassky
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Predictive Data Modeling and the Nature of Scientific Discovery
Prof. Vladmir Cherkassky Fellow of IEEE; ECE Department, University of Minnesota, Minneapolis, MN, USA; Former Director, NATO Advanced Study Institute (ASI); www.ece.umn.edu/users/cherkass/predictive_learning Served on the editorial boards of IEEE Transactions on Neural Networks, the Neural Networks Journal, the Natural Computing Journal and the Neural Processing Letters Date: July 12, 2010 Time: 06:00pm - 06:50pm Location: Ballroom 6 |
Scientific discovery involves interaction between two major components:
In classical science, the primary role belongs to a well-defined scientific
hypothesis which drives data collection and generation. So experimental
data is simply used to confirm or refute a scientific theory.
In the late 20-th century, the balance between facts and models in
scientific research has totally shifted, due to a growing use of digital
technology for data collection and recording. Nowadays, there is an
abundance of available data describing physical, biological and social
systems. Several new technologies, such as machine learning and data
mining, hold promise of ‘discovering’ new knowledge hidden in a sea of
data. Much of recent research in life sciences is data-driven, i.e. when
researchers try to establish ‘associations’ between certain genetic
variables and a disease. This is completely different from the classical
approach to scientific discovery. Whereas many machine learning and
statistical methods can easily detect correlations present in empirical
data, it is not clear whether such dependencies constitute new biological
knowledge. This is known as the problem of demarcation in the philosophy of
science, i.e. differentiating between true scientific theories and
metaphysical theories (beliefs).
Knowledge that can be extracted from empirical data is statistical in
nature, as opposed to deterministic first-principle knowledge in classical
science. Modern science is mainly about such an empirical knowledge, yet
there seems to be no clear demarcation between true empirical knowledge and
beliefs (supported by empirical data).
My talk will discuss methodological issues important for predictive data
modeling, i.e.,
These methodological issues are closely related to philosophical ideas,
dating back to Plato and Aristotle. The main points will be illustrated by
specific examples from an on-going project on prediction of
transplant-related mortality for bone-and-marrow transplant patients, in
collaboration with the University of Minnesota Medical School and the Mayo
Clinic.
Vladimir Cherkassky is Professor of Electrical and Computer Engineering at the University of Minnesota. He received Ph.D. in Electrical Engineering from University of Texas at Austin in 1985. His current research is on methods for predictive learning from data, and he has co-authored a monograph Learning From Data published by Wiley in 1998. Prof. Cherkassky has served on the Governing Board of INNS. He has served on editorial boards of IEEE Transactions on Neural Networks, the Neural Networks Journal, the Natural Computing Journal and the Neural Processing Letters. He served on the program committee of major international conferences on Artificial Neural Networks. He was Director of NATO Advanced Study Institute (ASI) From Statistics to Neural Networks: Theory and Pattern Recognition Applications held in France, in 1993. He presented numerous tutorials on neural network and statistical methods for learning from data. In 2007, he became Fellow of IEEE, for ‘contributions and leadership in statistical learning and neural network research’.
