Instructors

Mai obtained Ph.D. in Chemical Biology from McMaster University in 2019. Her Ph.D. research focused on metabolomics of irritable bowel syndrome and inflammatory bowel disease. Later, she worked as a postdoc under Dr. Jeff Xia at McGill University and became more familiar with R programming and computational work in the field of metabolomics. Currently, she works under Dr. David Wishart to make metabolomics-based personalized health assessment more accessible in partnership with an industrial partner.

Dr. Griffith’s research is focused on the development of genomics and bioinformatics methods as they apply to the study of cancer biology and medicine. A particular focus of his work is in the translation of genomics data from whole genome, exome and transcriptome sequencing into clinically actionable observations and personalized cancer therapies. He has led the development of key online informatics resources for cancer precision medicine such DGIdb, DoCM, CIViC and more.

He received a Ph.D. in Biochemistry from the University of Missouri-Columbia, USA, in 2002, where he studied the structure and dynamics of oncoviral proteins with high-resolution NMR methods. Mark conducted his post-doctoral research of protein structural biology in University of Michigan, USA, and then joined Dr. Wishart’s group at University of Alberta, Canada, to work on data analysis and software development in the fields of metabolomics, NMR, and protein structure and dynamics.

Mark Phillips works in comparative privacy and data protection law, particularly where it intersects with health data sharing. His academic background is in law and computer science, and he is a practicing member of the Quebec Bar Association. He works at the Centre of Genomics and Policy at McGill University as an Academic Associate, and is the co-chair of the Data Protection Task Team of the Global Alliance for Genomics and Health’s Research and Ethics Work Stream. His comparative legal research focuses on topics including cloud computing, the identifiability of personal data, bioinformatics, and open data.

Dr. Hirst’s research aims to further our understanding of the role of epigenetic dysfunction in cancer initiation and progression and to translate this knowledge into improved health outcomes for Canadians. International efforts to characterize genetic lesions in cancer genomes have revealed recurrent mutations in epigenetic modifiers and in some cases these can represent the sole driver. Understanding the functional implications of these mutations, their contribution to abnormal cellular differentiation and how emerging epigenetic therapeutics may counteract their effects represent the next critical steps towards translating this knowledge. In this context, Dr. Hirst is studying cancers that harbor highly recurrent gain and loss of function mutations to epigenetic modifiers, such as acute myeloid leukemia, synovial sarcoma, malignant rhabdoid tumor. His research involves the development and application of molecular and computational tools to measure epigenetic features and drive new insights into normal and pathogenic epigenetic regulatory control.

As a Bioinformatics manager of the TechDev unit, Mathieu ensures the integration and the support of new genomics technologies in the platform and he leads the software development. Prior to joining C3G, Mathieu was the team leader of the Bioinformatics service unit at Genome Quebec. He holds a PhD in Statistical Genetics from University Paris-Sud XI and a Master degree in Genetics from University Pierre-et-Marie-Currie (Paris VI).

Melanie is a registered Medical Laboratory Technologist and member of CMLTO in good standing. She has over 25 years of experience in Histology; 3 years in diagnostic Histology (Dynacare and Mount Sinai) and was team lead for the Pathology research program at UHN for 22 years. She has recently changed roles and has returned to Mount Sinai as Laboratory Manager for Mount Sinai Services.

Michael Hoffman creates predictive computational models to understand interactions between genome, epigenome, and phenotype in human cancers. His influential machine learning approaches have reshaped researchers’ analysis of gene regulation. These approaches include the genome annotation method Segway, which enables simple interpretation of multivariate genomic data. He is a Senior Scientist in and Chair of the Computational Biology and Medicine Program, Princess Margaret Cancer Centre and Associate Professor in the Departments of Medical Biophysics and Computer Science, University of Toronto. He was named a CIHR New Investigator and has received several awards for his academic work, including the NIH K99/R00 Pathway to Independence Award, and the Ontario Early Researcher Award.

Mike Wu is a recent graduate from Langara College with a Bachelor of Science in BIoinformatics. He is now doing his graduate studies at UBC in bioinformatics (specialized in machine learning in metabolomics). Mike has hands-on experience in a couple of bioinformatics projects, including single-cell RNA seq pipeline, metagenomics assembly and annotation, and blastInR package development, where R programming is mainly used for app development, data analysis, and pipeline construction.

Mohamed is a Computational Systems Biologist and Principal Scientist leading the Bioinformatics and Systems Biology Lab (BSBL) at the Vaccine and Infectious Disease Organization (VIDO), University of Saskatchewan. He received his MSc and PhD in Computational Systems Biology from Keio University (Tokyo, Japan) and completed his postdoctoral training in bioinformatics at Kyoto University and the University of Toronto. Mohamed’s interdisciplinary research profile bridges biology, computer science, and public health.

The focus of Dr. Langille’s research is to better understand human-microbial interactions and how that can be used to improve human health. This includes leveraging novel genomic technologies and developing improved bioinformatic methods to process and integrate multi-omic data to aid in biological interpretation. These discoveries will hopefully lead to novel applications for diagnosis and therapeutics.