Professor and founding Scientific Director of the Alberta Innovates Centre for Machine Learning, Computing Science, U of Alberta
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 and the founding Scientific Director and Fellow-in-Residence of the Alberta Innovates Centre for Machine Learning, which won the ASTech Award for “Outstanding Leadership in Technology” in 2006. He has been Program Chair for the 2004 “Int’l Conf. on Machine Learning”, Conference Chair for 2006 “Int’l Conf. on Machine Learning”, Editor-in-Chief for “Computational Intelligence”, and is serving on the editorial boards of a number of other journals. He was elected a Fellow of the AAAI (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. In May 2021 Russ Greiner was awarded the Canadian AI Association Lifetime Achievement Award.
He has published over 200 refereed papers and patents, most in the areas of machine learning and knowledge representation, including 4 that have been awarded Best Paper prizes. The main foci of his current work are (1) bioinformatics and medical informatics; (2) learning and using effective probabilistic models and (3) formal foundations of learnability.
L Kumar, R Greiner. 2019. Gene expression based survival prediction for cancer patients—A topic modeling approach. PloS one 14 (11), e0224446
M Cutumisu, C Vasquez, M Uhlich, PH Beatty, H Hamayeli-Mehrabani, et al. 2019. Prospect: a predictive research online system for prostate cancer tasks. JCO clinical cancer informatics 3, 1-12
F Aminmansour, A Patterson, L Le, Y Peng, D Mitchell, F Pestilli. Learning Macroscopic Brain Connectomes via Group-Sparse Factorization. 2019.NeurIPS, 8847-8857
S Tian, Y Djoumbou-Feunang, R Greiner, DS Wishart. 2019. CypReact: a software tool for in silico reactant prediction for human cytochrome P450 enzymes.
A Narasimhan, R Greiner, OF Bathe, V Baracos, S Damaraju. 2019. Differentially expressed alternatively spliced genes in skeletal muscle from cancer patients with cachexia. Journal of cachexia, sarcopenia and muscle 9 (1), 60-70
M Uhlich, R Greiner, B Hoehn, M Woghiren, I Diaz, T Ivanova, A Murtha. 2019. Improved Brain Tumor Segmentation via Registration-Based Brain Extraction. Forecasting 1 (1), 1-11
F Seyednasrollah, DC Koestler, T Wang, SR Piccolo, R Vega, R Greiner. 2019. A DREAM Challenge to build prediction models for short-term discontinuation of docetaxel in metastatic castration-resistant prostate cancer. JCO clinical cancer informatics 1, 1-15
A Narasimhan, S Ghosh, C Stretch, R Greiner, OF Bathe, V Baracos. 2019. Small RNAome profiling from human skeletal muscle: novel miRNAs and their targets associated with cancer cachexia. Journal of cachexia, sarcopenia and muscle 8 (3), 405-416
Guinney J, Wang T, Laajala TD, Winner KK, Bare JC, Neto EC, Khan SA, Peddinti G, Airola A, Pahikkala T, Mirtti T, Yu T, Bot BM, Shen L, Abdallah K, Norman T, Friend S, Stolovitzky G, Soule H, Sweeney CJ, Ryan CJ, Scher HI, Sartor O, Xie Y, Aittokallio T, Zhou FL, Costello JC; Prostate Cancer Challenge DREAM Community. Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data. Lancet Oncol. 2017 Jan;18(1):132-142
S. Ravanbakhsh, P. Liu, T. Bjorndahl, R. Mandal, J. Grant, M. Wilson, R. Eisner, I. Sinelnikov, X. Hu, C. Luchinat, R. Greiner, D. Wishart. “Accurate, fully-automated NMR spectral profiling for metabolomics”. PLoS One, 10(5), May 2015.
F. Allen, A. Pon, M. Wilson, R. Greiner, D. Wishart. “CFM-ID: A web server for annotation, spectrum prediction and metabolite identification from tandem mass spectra”. Nucleic Acids Research (NAR), June 2014.
F. Allen, R. Greiner, D. Wishart. “Competitive fragmentation modeling of ESI-MS/MS spectra for putative metabolite identification”. Metabolomics, June 2014.
M. Bastani, L. Vos, N. Asgarian, J. Deschenes, K. Graham, J. Mackey, R. Greiner. “A Machine Learned Classifier that uses Gene Expression Data to Accurately Predict Estrogen Receptor Status”. PLoS One, November 2013.
M. Hajiloo, B. Damavandi, M. Hooshsadat, F. Sangi, J. Mackey, C. Cass, R. Greiner, S. Damaraju. “Breast Cancer Prediction Using Genome Wide Single Nucleotide Polymorphism Data “. BMC Bioinformatics, 14(Suppl 13), pp S3, October 2013.
S. Ravanbakhsh, M. Gajewski, R. Greiner, J. Tuszynski. “Determination of the optimal tubulin isotype target as a method for the development of individualized cancer chemotherapy”. Theoretical Biology and Medical Modelling, April 2013
C. Stretch, S. Khan, N. Asgarian, R. Eisner, S. Vaisipour, S. Damaraju, O. Bathe, H. Steed, R. Greiner, V. Baracos. “Effects of sample size on differential gene expression, rank order and prediction accuracy of a gene signature”. PLoS One, 8(6), April 2013.