Instructors

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.

Nikta is a PhD student in the Medical Biophysics program at the University of Toronto. She completed her Bachelor of Science in Microbiology and her Master of Science in Bioinformatics. For her MSc thesis, Nikta worked on developing supervised algorithms for classifying cancer-specific somatic mutations. Her research interests include application of machine learning algorithms in pharmacogenomic analysis, cancer diagnosis and personalized medicine.

The goal of Professor Basu’s research is to design, validate, and apply innovative and sustainable approaches (focused on toxicogenomics) to address the most pressing societal concerns over toxic chemicals in our environment. Professor Basu’s research is multidisciplinary (bridges environmental quality and human health), inter-sectoral (most projects driven by stakeholder needs, notably government and communities), and driven by environmental justice concerns.

Dr. Griffith’s research is focused on the development of personalized medicine strategies for cancer using genomic technologies. He develops and uses bioinformatics, machine learning and clinical statistics for the analysis of high throughput sequence data and identification of biomarkers for diagnostic, prognostic and drug response prediction. He has led the development of key online informatics resources such as DGIdb, CIViC, GenVisR and more.