Machine Learning & AI for Healthcare

December 1 - 2, 2020
| Digital Summit


Professor, Department of Computing Science
University of Alberta

Russ Greiner focuses on developing and improving applications of machine learning in medicine, providing solutions for specific real-world problems across a range of clinical considerations. He works closely with clinicians and researchers in medicine (in psychiatry, oncology, cardiovascular, diabetes, and other areas), metabolomics and other disciplines to develop data-driven tools that assist practitioners with screening, diagnosis, prognosis and treatment planning in physical and mental health. Within the field of computational psychiatry, Russ uses machine learning on fMRI (functional magnetic resonance imaging) and other clinical data to develop new ways of diagnosing schizophrenia and for assessing the severity of a range of symptoms. These techniques can also be used across a range of psychiatric disorders including attention deficit hyperactivity disorder and depression. In the area of precision medicine, Russ works with colleagues in healthcare to develop methods for recommending patient-specific plans for the treatment of diseases such as cancer or diabetes and for predicting individual health outcomes. Russ is also interested in building better algorithms that learn from experience, working to produce more robust and effective machine learning systems.

After earning a PhD from Stanford, Russ Greiner worked in both academic and industrial research before settling at the University of Alberta, where he is now a Professor in Computing Science. He was a founding researcher of Amii, where he is also a Fellow in Residence. He has been the Conference and Program Chair for the International Conference on Machine Learning and Editor-in-Chief for Computational Intelligence. Russ serves on the editorial boards of a number of other journals including the Journal of Machine Learning Research, Machine Learning for Healthcare, and the Artificial Intelligence Journal. He was elected a Fellow of AAAI (the Association for the Advancement of Artificial Intelligence) in 2007, and was awarded a McCalla Professorship in 2005-06 and a Killam Annual Professorship in 2007. He has published over 300 refereed papers and patents, most in the areas of machine learning and knowledge representation, including 4 that have been awarded Best Paper prizes. Over his career, Russ has supervised more than 110 M.Sc. and Ph.D. candidates and post-doctoral fellows.