People

Dr. Hong Gu is a professor of statistics in the department of Mathematics and Statistics, Dalhousie University. After receiving her PhD in Statistics from the University of Hong Kong in 1999, she worked as a postdoc in University of Waterloo for two years, then moved to Dalhousie University in 2001. Her research interests include multivariate data analysis methods, model selection and inference, molecular phylogenetic models, statistical data mining and statistical methodology development for omics data.

She graduated from Carnegie Mellon University in 2017 with a dual degree in Computational Biology and Chemistry. She is interested in developing machine learning models for improving current strategies of neoantigen design and prioritization for cancer immunotherapy.

Dr. Ido Hatam holds a PhD in microbiology and biotechnology from the University of Alberta and serves as an Instructor in the Department of Biology and Bioinformatics program at Langara College, where he is also a Principal Investigator at the College’s Applied Research Center. Dr. Hatam has developed multiple courses for the College’s Bioinformatics program focused on using R-based tools to analyze high-throughput biological data. His research group uses bioinformatics tools to construct and analyze synthetic microbial communities, and specializes in “Guerrilla Bioinformatics” – generating value-added knowledge through meta-analysis of large, publicly available biological datasets.

Igor Jurisica, PhD, DSc is a Senior Scientist at Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute and Data Science Discovery Centre for Chronic Diseases, Krembil Research Institute, Professor at University of Toronto and Visiting Scientist at IBM CAS. Since 2015 he has served as Chief Scientist at the Creative Destruction Lab, Rotman School of Management, and since 2021 he is a scientific director of the World Community Grid. His research focuses on integrative informatics and the representation, analysis and visualization of high-dimensional data to identify prognostic/predictive signatures, determine clinically relevant combination therapies, and develop accurate models of drug mechanism of action and disease-altered signaling cascades. He has published extensively on data mining, visualization and integrative computational biology, including multiple papers in Science, Nature, Nature Medicine, Nature Methods, J Clinical Oncology, J Clinical Investigations. He has been included in Thomson Reuters 2014, 2015 & 2016 lists of Highly Cited Researchers (http://highlycited.com), and The World’s Most Influential Scientific Minds: 2015 & 2014 Reports. In 2019, he has been included in the Top 100 AI Leaders in Drug Discovery and Advanced Healthcare list (Deep Knowledge Analytics, http://analytics.dkv.global). In 2023, he has been included in the Top 100 AI in Oncology leaders: https://platform.dkv.global/map/reports/ai-in-oncology-leaders/

Ishu has a BSc in electrical engineering and has interest in digital signal processing and data science. He has worked in the Wishart Research Group to apply machine learning to various applications in bioinformatics, biology, and metabolomics.

Jacek Majewski, Professor of Human Genetics at McGill University, began his adventure in science as a wannabe physicist and, veering through a brief stint with electrical engineering, eventually found his way to biology. He received a PhD in Evolutionary Biology from Wesleyan University in Middletown CT, and followed his then fiancée to New York City for a post-doc in statistical genetics with Dr. Jurg Ott at the Rockefeller University. When genome sequencing happened, a background in quantitative sciences proved useful, resulting in his involvement in multiple genomics projects aimed at understanding basic biology, hereditary disease, and cancer. After many years of denial, he was recently forced to admit that epigenetics does indeed exist, which led to ongoing interest in functional epigenomics.

Jacqueline is a postdoctoral fellow in Dr. William Wong’s lab at University of Waterloo’s School of Pharmacy. Her current work focuses on the development of pharmacoeconomic models for health outcomes research. Her PhD thesis entailed a broad-scale comparison of different imputation methods on trait datasets and an investigation of how these methods impact statistical inferences.

James Green (PhD Queen’s University, 2005) is a full professor in the Department of Systems and Computer Engineering at Carleton University. His research focuses on machine learning challenges in biomedical informatics, particularly in the presence of class imbalance and the prediction of rare events. Current research projects include the prediction of protein structure, function, and interaction; the use of supervised and semi-supervised machine learning for the identification of microRNA in unique species; unobtrusive and non-contact neonatal patient monitoring; and the acceleration of scientific computing.

Our research focuses on the development of new algorithms, methods and software for analyzing genome sequencing data.

Jasmine received her Bachelor’s degree in Biology, majoring in Molecular Biology and minoring in Marine Biology, from the University of New Brunswick in April 2013. She then pursued her Master’s degree in Epidemiology from the Department of Epidemiology, Biostatistics and Occupational Health from McGill University in May 2016. Her research interest is integrating microbiome and metabolomics data to gain deep functional insights.

My research investigates how the genome functions in stem cells to regulate self-renewal and differentiation. We often think about transcription as occurring on a particular gene in a linear manner whereas the nucleus is a three dimension organelle into which the genome is folded and organised. Within this folded structure DNA regulatory sequences physically contact the genes they regulate forming tissue-specific chromatin loops. We use CRISPR Genome Editing, Molecular Biology and Cellular Imaging techniques combined with Genome-Wide Sequencing approaches and Bioinformatics analysis to investigate the mechanisms that underlie tissue-specific regulation of gene expression and genome folding.

Dr. Jennifer Geddes-McAlister is an Associate Professor in the Department of Molecular and Cellular Biology at the University of Guelph and the Canada Research Chair in the Proteomics of Fungal Disease in One Health. Her lab applies mass spectrometry-based proteomics and bioinformatics tools to investigate host-pathogen interactions with a focus on One Health approaches to overcoming fungal disease. She was recently awarded an Alumni Achievement Award from the University of Lethbridge, a Research Excellence Award from the University of Guelph and multiple early career researcher awards from the Government of Ontario and scientific societies. She is Director of the Bioinformatics Graduate Programs at the University of Guelph, President of the Canadian National Proteomics Network, co-founder of the Canadian Proteomics and Artificial Intelligence Consortium, and founder of ‘Moms in Proteomics’ an initiative dedicated to recognizing and supporting mothers in STEM.