Instructors

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.

Kelsy is a PhD candidate in the Molecular Cell Biology program at Washington University in St. Louis. She completed her undergraduate degree at Mercer University in 2016, where she earned a B.S. in Biochemistry and Molecular Biology. She is interested in developing methods to analyze multiple types of sequencing data in order to better understand regulatory mutations and splicing within cancer, particularly with respect to personalized cancer vaccine design. Currently, she is involved with [2]DGIdb, [3]RegTools, ORegAnno and analysis of several breast cancer clinical cohorts. She is also part of the Precision Medicine Pathway and Cancer Biology Pathway at WashU, which allows to better understand how she can translate genomics and informatics into the clinic more efficiently.

Laura Hug seeks to define microbial diversity and function at contaminated sites using culture-based and culture-independent methods, generating a blueprint of which species are there and which pathways are active. Her research expands our understanding of the tree of life, while simultaneously developing solutions to address the impacts of human activities on the environment.

Lauren has an MSc in Biostatistics from the University of Toronto and has previously worked as a Biostatician for two pediatric psychiatric genetics labs at SickKids. She is currently an MSc student in Dr. Anna Goldenberg’s lab. In her work, Lauren is focused on developing and applying statistical machine learning methods primarily in the area of data integration for improved translational discovery in the fields of genetics and genome biology. Lauren has also created custom R programming and data analysis courseware and taught over 200 trainees and scientists in the SickKids research program.

Dr. Lawrence Heisler manages the Genome Sequencing Informatics Analysis team as part of the Genomics Program at OICR. His team develops analysis workflows and production pipelines in support of clinically accredited and research-use only sequencing assays. He holds a graduate degree in Physiology from Queen’s University in Kingston Ontario, and has over 20 years of experience with analysis of genomic data.

Prior to joining OICR in 2006, Dr. Stein played an integral role in many large-scale data initiatives at Cold Spring Harbor Laboratory and at the Massachusetts Institute of Technology (MIT) Genome Center. He led the development of the first physical clone map of the human genome, and ran the data coordinating centre and the data portal for the SNP Consortium and the HapMap Consortium. Dr. Stein has also led the creation and development of Wormbase, a community model organism database for C. elegans, and Reactome, which is now the largest open community database of biological reactions and pathways. At OICR, Dr. Stein has led several international cancer data sharing and research initiatives, including the creation and development of the data coordination centre for the International Cancer Genome Consortium and other related projects. He continues to collaborate with national and international partners to create and promote data sharing standards, protocols and implementations.

Lusine received her Ph.D. in Mathematics under the supervision of prof. V. Bogachev from Moscow State University in the area of nonlinear stochastic equations for measures. She works in Dr. D. Wishart’s computational group, where she learned machine learning and Python and developed tools for applications in bioinformatics. She also works in research involving applications of EPR spectroscopy at the Applied Pharmaceutical Innovation.

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.