Russ Greiner focuses on survival prediction and learning models that produce objective and actionable outcomes.

Personalizing Healthcare

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 (Adjunct in Psychiatry) and the founding Scientific Director of the Alberta Machine Intelligence Institute. He has been Program/Conference Chair for various major conferences, and has served on the editorial boards of a number of relevant journals. He was elected a Fellow of the AAAI (Association for the Advancement of Artificial Intelligence), was awarded a McCalla Professorship and a Killam Annual Professorship; in 2021, received the CAIAC Lifetime Achievement Award and became a CIFAR AI Chair.  In 2022, the Telus World of Science museum honored him with a panel, and he received the (UofA) Precision Health Innovator Award, then in 2023, he received the CS-Can | Info-Can Lifetime Achievement Award.  In 2024, he shared the Brockhouse Prize with David Wishart, for their joint work on "Machine Learning for Metabolomics". For his mentoring, he received a 2020 FGSR Great Supervisor Award, then in 2023, the Killam Award for Excellence in Mentoring.  He has published over 350 refereed papers, most in the areas of machine learning and recently medical informatics, including 6 that have been awarded Best Paper prizes. The main foci of his current work are (1) bio- and medical- informatics; (2)  survival prediction and (3) formal foundations of learnability.