Experts

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.

Jérôme Waldispühl is an associate professor of Computer Science at McGill University. He conducts research in RNA structural bioinformatics and cheminformatics. He also pioneered the use of video games to engage the public in genomic research with Phylo (2010), Colony B (2016), Borderlands Science (2020) and Project Discovery Phase 3 (2020), which have engaged millions of participants worldwide.

Dr. Jiarui Ding has been an Assistant Professor in the Department of Computer Science at the University of British Columbia. His academic journey includes earning a Ph.D. from UBC, where he was advised by Drs. Sohrab Shah and Anne Condon, and a postdoctoral fellowship at the Broad Institute of MIT and Harvard under the guidance of Dr. Aviv Regev from 2017 to 2021. He holds the prestigious position of Canada Research Chair in Machine Learning and Single-cell Analysis. He leads an interdisciplinary group at UBC. His research primarily focuses on single-cell genomics, where he has contributed to three key areas: developing computational models, using single-cell RNA-sequencing (scRNA-seq) to study diseases, and benchmarking various scRNA-seq technologies.

Dr John Parkinson is a computational biologist whose research interests focus on the impact of microbiota on human health. After completing his PhD at the University of Manchester, studying molecular self-assembly, John spent a year at the University of Manitoba investigating diatom morphogenesis. In 1997, John moved to Edinburgh where he applied computer models to study the evolution of complement control proteins with Dr Paul Barlow. With the emergence of high throughput sequencing, John then led the bioinformatics efforts associated with the parasitic nematode expressed sequence tag project, responsible for the processing and curation of sequence data from 30 species of parasitic nematodes. John was recruited to the Hospital for Sick Children in 2003 and was promoted to Senior Scientist in 2009. He holds cross-appointments in both the departments of Biochemsitry and Molecular Genetics at the University of Toronto. Current lab interests center on the role of the microbiome in health and disease as well as the mechanisms that allow  pathogens and parasites to survive and persist in their human hosts.  Key to this research is the integration of computational systems biology analyses with comparative genomics to explore the evolution and operation of microbial pathways driving pathogenesis. Findings from our research programs are helping guide new strategies for therapeutic intervention.

I am a computational biologist trained in evolutionary genetics, and I use probabilistic modeling to investigate functional divergence within complex biological systems. I am cross-appointed at Dalhousie University in the Departments of Biology and Mathematics & Statistics. I supervise an inter-disciplinary research program with graduate students in both Biology and Statistics. My research exploits the simultaneous pursuit of biological questions and modeling advancements. I have traditionally worked in the area modeling and inference of functional divergence at the gene and genome level. More recent work involves the development of new models and methods to study multi-level processes in the context of explicit changes in organism phenotype. We have developed new models of the joint analysis of gene and phenotype evolutionary dynamics along a phylogenetic tree. We have also developed new process models to explore multi-level selection theory (MLST) in the context of gene – species – community level selective pressures.

Dr. Kong is a professor at the University of Toronto, where he serves as the director of the Artificial Intelligence (AI) and Mathematical Modeling Lab. Additionally, he is the Director of the Africa-Canada Artificial Intelligence and Data Innovation Consortium and the Global South Artificial Intelligence for Pandemic and Epidemic Preparedness and Response Network. He is also the Regional Node Liaison to the steering committee of the Canadian Black Scientist Network. He obtained his Ph.D. in Mathematics with a certificate in AI from the University of Alberta, his MSc in Engineering Mathematics from the University of Hamburg, Germany, and the University of L’Aquila, Italy. His B.Sc. in Computer Science and Mathematics was acquired at the University of Buea, Cameroon, and his B.Ed. in Mathematics was earned at University of Yaounde I, Cameroon. He did a 2-years of postdoc at Princeton University. Dr. Kong is an expert in AI, data science, mathematical modeling, and mathematics education. His principal research program focuses on designing and deploying AI, data science, and mathematical methodologies and technologies to build equitable, resilient governance strategies and increase societal preparedness for future global pandemics and climate disasters.

Dr. Julie Hussin is an Associate Professor in the Department of Medicine at the Université de Montréal (UdeM) and holds a Junior 2 fellowship from Fonds de Recherche du Québec en Santé. She is a core member of the Data Institute (IVADO) and an associate academic member at Mila (Quebec AI Institute). She is an expert in bioinformatics and evolutionary genomics, with extensive experience working with multi-omics datasets from large population cohorts. Her current research focuses on applying data science and machine learning techniques to improve cardiovascular health and pandemic preparedness. She also serves as the Chair of Graduate Studies in Bioinformatics at UdeM and her commitment to Inclusion, Diversity, Equity, and Access (IDEA) is evident through her active participation in IDEA committees for the CIFAR AI for Health Imaging Solution Network and CMDO Network.

We are a computational biology group at McGill University School of Medicine’s Meakins-Christie Laboratories. Our mission is to unravel the intricacies of cell dynamics in various diseases, including developmental disorders, pulmonary diseases, and cancers. Deciphering these dynamics is crucial for comprehending disease pathogenesis and discovering novel therapies. Our research leverages cutting-edge single-cell technologies, which offer unprecedented insights into individual cell states. We harness these technologies to drive discoveries and medical innovations in developmental and cancer biology. However, the complexity and heterogeneity of these diseases pose significant challenges in analyzing single-cell data. Our primary focus is on developing machine learning techniques, particularly probabilistic graphical models, to comprehensively analyze, model, and visualize single-cell and bulk omics data, preferably in longitudinal or spatial contexts. These computational models deepen our understanding of cell dynamics across diverse biological systems. Ultimately, our work aims to advance public health through machine-learning-driven diagnostic and therapeutic strategies.

Dr. Jüri Reimand is a principal investigator at the Ontario Institute for Cancer Research (OICR) and associate professor at the University of Toronto, Canada. His lab focuses on computational biology, cancer genomics, and development of statistical and machine-learning methods. Areas of interest include interpretation of the non-coding genome and driver mutations, integrative analysis of multi-omics data through pathway and network information, and discovery of molecular biomarkers.

Kay is a Professor in the School of Computing Science at Simon Fraser University, Canada. His research interests are in RNA structure prediction, RNA visualization, CRISPR-Cas sgRNA design and other related applications. He enjoys developing innovative machine learning and optimization techniques for practical applications, but he is also interested in studying and developing new fundamental machine learning approaches. One such problem is developing neural networks that adapt their activation functions based on the underlying task. His lab developed the RnaPredict software for RNA folding, the jViz.RNA package for RNA visualization including pseudoknots, and the EvoDNN software for self-adaptive deep neural networks.

Keegan (she/her/hers) is an assistant professor in the Department of Statistics at the University of British Columbia and an investigator in the Centre for Molecular Medicine and Therapeutics at the BC Children’s Hospital Research Institute. She is also a faculty member in the Bioinformatics and Genome Science and Technology graduate programs at UBC. Her research group tackles the challenging task of uncovering meaningful biological insights from large-scale genomic experiments. Innovative technologies now allow scientists to probe the genome in more dimensions and at higher resolution than ever before, providing a wealth of information for studying the genomic basis of complex traits. However, discoveries from these new technologies can often be masked by technical artifacts, systematic biases, or low signal-to-noise ratio – think “needle in a haystack”. Keegan leads a team of researchers that focuses on developing novel frameworks and rigorous inferential procedures that exploit the increased scope and scale of high-throughput sequencing data, with the ultimate goal of uncovering new molecular signals in cancer, child health, and development.