People
Lingling Jin received her Ph.D. degree in Computer Science specializing in Bioinformatics from the University of Saskatchewan. She is currently an Assistant Professor of Computer Science at the University of Saskatchewan and an adjunct faculty member at Thompson Rivers University. Her primary research interest is in the computational modelling of genome evolution and various aspects of Comparative Genomics and Phenomics with specific attention on flowering plants. Her research aims to improve our understanding of plant genomes and the consequences of genome evolution.
Dr. Linglong Kong is a Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta, holding a Canada Research Chair in Statistical Learning and a Canada CIFAR AI Chair. He is a Fellow of the American Statistical Association (ASA) and the Alberta Machine Intelligence Institute (Amii), with over 120 peer-reviewed publications in leading journals and conferences such as AOS, JASA, JRSSB, NeurIPS, ICML, and ICLR. Dr. Kong received the 2025 CRM-SSC Prize for outstanding research in Canada. He serves as Associate Editor for several top journals, including JASA and AOAS, and has held leadership roles within the ASA and the Statistical Society of Canada. Dr. Kong’s research interests include high-dimensional and neuroimaging data analysis, statistical machine learning, robust statistics, quantile regression, trustworthy machine learning, and artificial intelligence for smart health.
Dr. Strug is a Professor in the Departments of Statistical Sciences, Computer Science and cross-appointed in Biostatistics at the University of Toronto and is a Senior Scientist in the Program in Genetics and Genome Biology at the Hospital for Sick Children. She is the Lead of the Canadian Cystic Fibrosis (CF) Gene Modifier Study, Co-Lead of the International CF Gene Modifier Consortium, Director of the Ontario Regional Centre of the Canadian Statistical Sciences Institute (CANSSI) and the inaugural Academic Director of the Data Science Initiative (DSI) at the University of Toronto. As a statistical geneticist, her research focuses on the development of novel statistical approaches to analyze and integrate multi-omics data to identify genetic contributors to complex human disease. She has received several honours and awards including the Tier 1 Canada Research Chair in Genome Data Science.
Dr. Lourdes Peña-Castillo,PhD, is a Professor in the Departments of Computer Science and Biology (jointly appointed) in the Faculty of Science at Memorial University of Newfoundland (MUN). She leads the Bioinformatics lab at MUN which is focused on the application of machine learning-based methods to solve microbiology problems. Before coming to MUN, Dr. Peña-Castillo obtained her Ph.D. from the Otto-von-Guericke University in Germany and did a 3-year postdoc in the Banting and Best Department of Medical Research at the University of Toronto.
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.
Marcel Turcotte is a Computer Science Professor at the University of Ottawa’s School of Electrical Engineering and Computer Science. His group applies machine learning, algorithm design, and efficient data structures to solve complex bioinformatics problems such as identifying cell type-specific DNA signatures of transcription factor binding, classifying non-coding RNA sequences, and determining RNA virus-host susceptibility.