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Ryan J. Urbanowicz, Ph.D.
62Adjunct Assistant Professor of Biostatistics and Epidemiology
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Department: Biostatistics and Epidemiology
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Contact information
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403 Blockley Hall
1a 423 Guardian Drive
42 University of Pennsylvania
Philadelphia, PA 19104-6116
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1a 423 Guardian Drive
42 University of Pennsylvania
Philadelphia, PA 19104-6116
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Office: 802-299-9461
32 Fax: 215-573-3111
32 Lab: 215-746-4225
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32 Fax: 215-573-3111
32 Lab: 215-746-4225
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Education:
21 9 B.S. 35 (Biological and Environmental Engineering) c
2b Cornell University, 2004.
21 a M.Eng 2e (Biological and Environmental Eng.) c
2b Cornell University, 2005.
21 a Ph.D. 34 (Ph.D. in Genetics/Computational Biology) c
2a Dartmouth College, 2012.
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Permanent link21 9 B.S. 35 (Biological and Environmental Engineering) c
2b Cornell University, 2004.
21 a M.Eng 2e (Biological and Environmental Eng.) c
2b Cornell University, 2005.
21 a Ph.D. 34 (Ph.D. in Genetics/Computational Biology) c
2a Dartmouth College, 2012.
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c9 Created Educational YouTube Video: Learning Classifier Systems in a Nutshell. https://www.youtube.com/watch?v=CRge_cZ2cJc 2017.
13d Olson, R.S, Urbanowicz, R.J., Moore, J.H.: Automating biomedical data science through tree-based pipeline optimization. Springer Lecture Notes in Computer Science Page: 123-137, 2016 Notes: Highlight: Won a best paper award in the EvoBIO track.
e8 Urbanowicz, R.J., Browne, W.: Introduction to learning classifier systems. Springer, New York, NY. Springer, New York, NY, 2016 Notes: Available on amazon.com.
18a Olson, R.S, Urbanowicz, R.J., Moore, J.H.: Evaluation of a tree-based pipeline optimization tool for automating data science. Proceedings of the Genetic and Evolutionary Computing Conference. ACM Press, Page: 485-492, 2016 Notes: Highlight: Won a best paper award in the Evolutionary Machine Learning Track at GECCO’16.
110 Urbanowicz, R.J., Olson, R.S, Moore, J.H.: Pareto inspired multi-objective rule fitness for noise-adaptive rule-based machine learning. Springer Lecture Notes in Computer Science Page: 514-524, 2016.
173 Urbanowicz, R.J., Moore, J.H.: ExSTraCS 2.0: Description and evaluation of a scalable learning classifier system. Evolutionary Intelligence. 8(2-3): 89-116, 2015 Notes: Highlight: Solved the extremely complex 135-bit benchmark multiplexer problem directly for the first time reported in literature.
12a Urbanowicz, R.J., Moore, J.H.: Retooling fitness for noisy problems in a supervised Michigan-style learning classifier system. Proceedings of the Genetic and Evolutionary Computing Conference. ACM Press, Page: 591-598, 2015.
108 Urbanowicz, R.J., Ramanand, N., Moore, J.H.: Continuous endpoint data mining with ExSTraCS. Proceedings of the Genetic and Evolutionary Computing Conference. ACM Press, Page: 1029-1036, 2015.
12f Urbanowicz, R.J., Bertasius, G., Moore, J.H.: An extended michigan-style learning classifier system for flexible supervised learning, classification, and data mining. Springer Lecture Notes in Computer Science Page: 211-221, 2014.
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Selected Publications
fc Olson, R.S., LaCava W., Orzechowski, P., Urbanowicz, R.J., Moore, J.H.: PMLB: A large benchmark suite for machine learning evaluation and comparison. BioData Mining December 2017.c9 Created Educational YouTube Video: Learning Classifier Systems in a Nutshell. https://www.youtube.com/watch?v=CRge_cZ2cJc 2017.
13d Olson, R.S, Urbanowicz, R.J., Moore, J.H.: Automating biomedical data science through tree-based pipeline optimization. Springer Lecture Notes in Computer Science Page: 123-137, 2016 Notes: Highlight: Won a best paper award in the EvoBIO track.
e8 Urbanowicz, R.J., Browne, W.: Introduction to learning classifier systems. Springer, New York, NY. Springer, New York, NY, 2016 Notes: Available on amazon.com.
18a Olson, R.S, Urbanowicz, R.J., Moore, J.H.: Evaluation of a tree-based pipeline optimization tool for automating data science. Proceedings of the Genetic and Evolutionary Computing Conference. ACM Press, Page: 485-492, 2016 Notes: Highlight: Won a best paper award in the Evolutionary Machine Learning Track at GECCO’16.
110 Urbanowicz, R.J., Olson, R.S, Moore, J.H.: Pareto inspired multi-objective rule fitness for noise-adaptive rule-based machine learning. Springer Lecture Notes in Computer Science Page: 514-524, 2016.
173 Urbanowicz, R.J., Moore, J.H.: ExSTraCS 2.0: Description and evaluation of a scalable learning classifier system. Evolutionary Intelligence. 8(2-3): 89-116, 2015 Notes: Highlight: Solved the extremely complex 135-bit benchmark multiplexer problem directly for the first time reported in literature.
12a Urbanowicz, R.J., Moore, J.H.: Retooling fitness for noisy problems in a supervised Michigan-style learning classifier system. Proceedings of the Genetic and Evolutionary Computing Conference. ACM Press, Page: 591-598, 2015.
108 Urbanowicz, R.J., Ramanand, N., Moore, J.H.: Continuous endpoint data mining with ExSTraCS. Proceedings of the Genetic and Evolutionary Computing Conference. ACM Press, Page: 1029-1036, 2015.
12f Urbanowicz, R.J., Bertasius, G., Moore, J.H.: An extended michigan-style learning classifier system for flexible supervised learning, classification, and data mining. Springer Lecture Notes in Computer Science Page: 211-221, 2014.
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