Dr. Stefan C. Kremer, Professor (School of Computer and Science, University of Guelph, ON, CANADA)
May 28, 2018 – 11:00 AM
DIISM, Artificial Intelligence laboratory (room 201), Siena SI
Biological systems, on all scales, are essentially information processing and control systems. From this perspective, analogies between biological processes and computational processes can be drawn. In particular, this talk will compare biological systems to computer systems and the practice of modern bioinformatics to that of software and hardware hacking. Machine learning techniques in bioinformatics can then be applied to reverse-engineer the informational and physical operating principles of organisms. As an example application we will present DNA barcoding and the opportunities for Machine Learning in this domain. This knowledge, in turn, can then be applied to medicine, conservation and food production.
STEFAN C. KREMER is a Professor of Computer Science at the University of Guelph, in Ontario, Canada. He is the co-editor of A Field Guide to Dynamical Recurrent Networks (IEEE Press, 2000), a number of journal papers on recurrent networks, grammar induction and automata theory, published in Neural Computation and the Journal of Machine Learning Research. Dr. Kremer has organized 3 workshops on the subjects of recurrent neural networks and semi-supervised learning. His research focuses on adaptive pattern recognition systems for structured data, in particular sequential information; and, his publications in the areas of recurrent neural networks, hidden Markov models, and grammar induction algorithms have provided new methods and better understandings of how to apply adaptive systems to the problem of recognizing and classifying information with sequential structure. Recently, he has applied his experience with sequence learning models to identify promoter regions in E. coli, assess the effectiveness of PCR primers, predict protein folds, improve the performance of third-gen DNA sequencing, and model transposon ecology and evolution.