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WORLDCOMP'10 Invited Lecture - Prof. Vladmir Cherkassky

Last modified 2010-06-22 18:18

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);
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:
      • facts, or observations of the Real World (or Nature);
      • Scientific theories (models), i.e. mental constructs, explaining this observed data.
    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.,
      • first-principle knowledge, empirical knowledge and beliefs;
      • understanding of uncertainty and risk,
      • predictive data modeling,
      • interpretation of predictive models.
    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’.

Academic Co-Sponsors
The Berkeley Initiative in Soft Computing (BISC)
University of California, Berkeley, USA

Collaboratory for Advanced Computing and Simulations (CACS)
University of Southern California, USA

Intelligent Data Exploration and Analysis Laboratory
University of Texas at Austin, Austin, Texas, USA

Harvard Statistics Department Genomics & Bioinformatics Laboratory
Harvard University, Cambridge, Massachusetts, USA

BioMedical Informatics & Bio-Imaging Laboratory
Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA

Hawkeye Radiology Informatics, Department of Radiology, College of Medicine, University of Iowa, Iowa, USA

Minnesota Supercomputing Institute
University of Minnesota, USA

Center for the Bioinformatics and Computational Genomics
Georgia Institute of Technology, Atlanta, Georgia, USA

Medical Image HPC & Informatics Lab (MiHi Lab)
University of Iowa, Iowa, USA

The University of North Dakota
Grand Forks, North Dakota, USA

Knowledge Management & Intelligent System Center (KMIS)
University of Siegen, Germany

UMIT, Institute of Bioinformatics and Translational Research, Austria
SECLAB of University of Naples Federico II
University of Naples Parthenope, & Second University of Naples, Italy

National Institute for Health Research
World Academy of Biomedical Sciences and Technologies
High Performance Computing for Nanotechnology (HPCNano)
Supercomputer Software Department (SSD), Institute of Computational Mathematics & Mathematical Geophysics, Russian Academy of Sciences

International Society of Intelligent Biological Medicine

The International Council on Medical and Care Compunetics

The UK Department for Business, Innovation and Skills

VMW Solutions Ltd.
Scientific Technologies Corporation
HoIP - Health without Boundaries

Space for Earth Foundation
Medical Modeling and Simulation Database (EVMS) of Eastern Virginia Medical School & the American College of Surgeons

Corporate Sponsor

Other Co-Sponsors
Manjrasoft (Cloud Computing Technology company), Melbourne, Australia

Hodges' Health


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