Over the last 35 years, Russ Greiner has explored foundational topics in machine learning, to develop techniques that find predictive patterns in data to produce learned models that can be used to make good predictions for new instances. Recently, most of the task have been medical: (1) producing models that can accurately predict whether a women’s breast cancer will relapse, based on information in her genes; (2) whether a person has adenoma, based on metabolites (small molecules) in his/her urine; (3) whether a person has schizophrenia, based on an fMRI scan of his/her brain; and (4) how long a patient will live after receiving a new liver transplant, based on various factors about both the recipient and the donor. Building these systems requires addressing many important foundational topics, such as novel ways to combine related data acquired under different conditions; effective approaches for counterfactual reasoning; and useful new approaches to survival prediction.
- Great Supervisor Award, University of Alberta, 2020
- Fellow of the AAAI, Association for the Advancement of Artificial Intelligence, 2007
- Killam Annual Professorship, Killam, 2007
- McCalla Professorship, University of Alberta, 2005-06
- Multiple best paper prizes, ie., IJCAI, CSCSI.
- Haider, H., Hoehn, B., Davis, S., & Greiner, R. (2020). Effective ways to build and evaluate individual survival distributions. Journal of Machine Learning Research, 21(85), 1-63.
- Kumar, L., & Greiner, R. (2019). Gene expression based survival prediction for cancer patients—A topic modeling approach. PloS one, 14(11), e0224446.
- Kalmady, S. V., Greiner, R., Agrawal, R., Shivakumar, V., Narayanaswamy, J. C., Brown, M. R., ... & Venkatasubramanian, G. (2019). Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning. npj Schizophrenia, 5(1), 1-11.
- Allen, Felicity, Allison Pon, Michael Wilson, Russ Greiner, and David Wishart. "CFM-ID: a web server for annotation, spectrum prediction and metabolite identification from tandem mass spectra." Nucleic acids research 42, no. W1 (2014): W94-W99.
- Greiner, R., Su, X., Shen, B., & Zhou, W. (2005). Structural extension to logistic regression: Discriminative parameter learning of belief net classifiers. Machine Learning, 59(3), 297-322.
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