…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. This site aims to orient visitors to our past, present, and future research/goals as well as offer relevant resources and links.
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.
My name is Ryan Urbanowicz. I lead the newly formed URBS lab. I am 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 am also a Senior Fellow in the Institute for Biomedical Informatics and affiliated with the Graduate Group in Genetics and Computational Biology (GCB).
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.
- 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.
- Urbanowicz, Ryan J., Christopher Lo, John H. Holmes, and Jason H. Moore. "Attribute tracking: strategies towards improved detection and characterization of complex associations." In Proceedings of the Genetic and Evolutionary Computation Conference, pp. 553-560. ACM, 2018.
- Urbanowicz, Ryan J., Ben Yang, and Jason H. Moore. "Problem Driven Machine Learning by Co-evolving Genetic Programming Trees and Rules in a Learning Classifier System." In Genetic Programming Theory and Practice XV, pp. 55-71. Springer, Cham, 2018.
- Verma, S.S., Lucas, A., Zhang, X., Veturi, Y., Dudek, S., Li, B., Li, R., Urbanowicz, R., Moore, J.H., Kim, D. and Ritchie, M.D., 2018. Collective feature selection to identify crucial epistatic variants. BioData mining, 11(1), p.5.
- Le, Trang T., Ryan J. Urbanowicz, Jason H. Moore, and Brett A. McKinney. "Statistical Inference Relief (STIR) feature selection." bioRxiv (2018): 359224.
- Olson, Randal S., William La Cava, Patryk Orzechowski, Ryan J. Urbanowicz, and Jason H. Moore. "PMLB: a large benchmark suite for machine learning evaluation and comparison." BioData mining 10, no. 1 (2017): 36.
- Olson, Randal S., Moshe Sipper, William La Cava, Sharon Tartarone, Steven Vitale, Weixuan Fu, Patryk Orzechowski, Ryan J. Urbanowicz, John H. Holmes, and Jason H. Moore. "A system for accessible artificial intelligence." arXiv preprint arXiv:1705.00594 (2017).
Collaboration and Hiring
As a new research group, the URBS Lab is always interested in engaging in new collaborations. Further we are planning to hire a post-doctoral researcher sometime over the next year, so keep an eye out for an official job posting or reach out to learn more.
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