Urbanowicz Lab

Welcome!

…to the URBS Lab (Unbounded Research in Biomedical Systems). Our primary goal is to develop, evaluate, and apply tools/methods that can be leveraged to improve our understanding of human health including etiology, diagnosis, prevention, and treatment. We hold a particular interest in 'nature inspired computing' methodologies. This site aims to orient visitors to our past, present, and future research/goals as well as offer relevant resources and links. 

Important Update:

Please note that as of December 2021, the URBS lab has moved to the Department of Computational Biomedicine at the Cedars-Sinai Medical Center in Los Angeles. I am in the process of applying for an appointment of Adjunct Professor within my former Department at UPenn.

Our Mission

The mission of the URBS lab is to:

• Facilitate machine learning/data mining comprehension and collaboration in biomedical applications.
• Challenge methodological norms.
• Develop and apply novel informatics and machine learning strategies that:
  • Minimize assumptions and allow for open ended discovery
  • Tackle the practical challenges of modern day biomedical analysis, i.e. (1) detection of complex, multivariate patterns of association, (2) large-scale (i.e. big) data, (3) data integration (types/sources), (4) imbalanced data, (5) missing (i.e. incomplete data), (6) model interpretability, and (7) computational expense.

Principle Investigator

My name is Ryan Urbanowicz.  I lead the newly formed URBS lab.  I am currently an Assistant Professor of Computational Biomedicine at the Cedars-Sinai Medical Center in Los Angeles, CA but still actively engage in numerous collaborations with students and faculty at UPenn. I was previously an Assistant Professor of Informatics in the Department of Biostatistics Epidemiology and Informatics at the Perelman School of Medicine of the University of Pennsylvania in Philadelphia.  I was formerly also a Senior Fellow in the Institute for Biomedical Informatics and affiliated with the Graduate Groups in Genetics and Computational Biology (GCB) and Epidemiology and Biostatistics (GGEB). 

My educational background is interdisciplinary, at the intersection of biology, engineering, computer science, and biostatistics.  I completed my PhD in genetics (with a focus on computational biology) at Dartmouth College, proceeded by a Masters and Bachelors of Biological Engineering at Cornell University.  I share an equal passion for research, teaching, and mentoring. Click HERE for my complete CV.

Highlighted Publications

• Zhang, R.F., Urbanowicz, R.J. A Scikit-learn Compatible Learning Classifier System. Proceedings of the Genetic and Evolutionary Computing Conference. ACM Press, pp. 1816-1823. (2020)
• Moore, J.H., Boland M.R., Camara, P.G., Chervitz, H., Gonzalez, G., Himes, B.B., Kim, D., Mowery D.L., Ritchie, M.D., Shen, L., Urbanowicz, R.J., Holmes, J.H. Preparing next-generation scientists for biomedical big data: Artificial intelligence approaches. Personalized Medicine (0) (2019)
• Sipper, Moshe, Ryan J. Urbanowicz, and Jason H. Moore. "To know the objective is not (necessarily) to know the objective function." BioData Mining. (2018): 21.
• Urbanowicz, Ryan J., Melissa Meeker, William La Cava, Randal S. Olson, and Jason H. Moore. "Relief-based feature selection: introduction and review." Journal of biomedical informatics 85 (2018): 189-203
• Urbanowicz, Ryan J., Randal S. Olson, Peter Schmitt, Melissa Meeker, and Jason H. Moore. "Benchmarking relief-based feature selection methods for bioinformatics data mining." Journal of biomedical informatics 85 (2018): 168-188.
• Olson, R.S, Urbanowicz, R.J., Moore, J.H., Automating biomedical data science through tree-based pipeline optimization. Springer Lecture Notes in Computer Science 9597, 123-137 (2016).
• 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). 
• Urbanowicz, R.J., Kiralis, J., Sinnott-Armstrong, N.A., Heberling, T., Fisher, J.M., Moore, J.H. GAMETES: A fast, direct algorithm for generating pure, strict, epistatic models with random architectures. BioData Mining 5, 16 (2012).
• Urbanowicz, R.J., Granizo-Mackenzie, A., Moore, J.H. An analysis pipeline with statistical and visualization-guided knowledge discovery for Michigan-style learning classifier systems. Computational Intelligence Magazine 7, 35-45 (2012).
• Urbanowicz, R.J., Moore, J.H. Learning classifier systems: A complete introduction, review and roadmap. Journal of Artificial Evolution and Applications 2009, 1-25 (2009).