People

Michael Brudno is a Professor in the Department of Computer Science at the University of Toronto, as well as the Chief Data Scientist at the University Health Network (UHN). He is also a faculty member at the Vector Institute for Artificial Intelligence and the Scientific Director of HPC4Health, a private computing cloud for Ontario hospitals. His main research interest is the development of computational methods for the analysis of clinical and genomic datasets, especially the capture of precise clinical data from clinicians using effective user interfaces, and its utilization in the automated analysis of genomes. This work focuses on the capture of structured phenotypic data from clinical encounters, using both refined User Interfaces, and mining of unstructured data (based on Machine Learning methodology), and the analysis of omics data (genome, transcriptome, epigenome) in the context of the structured patient phenotypes, mostly for rare diseases. His overall research goal is to enable the seamless automated analysis of patient omics data based on automatically captured information from a clinical encounter, thus streamlining clinical workflows and enabling faster and better treatments. After receiving a BA in Computer Science and History from UC Berkeley, Michael received his PhD from the Computer Science Department of Stanford University, working on algorithms for whole genome alignments. He completed a postdoctoral fellowship at UC Berkeley and was a Visiting Scientist at MIT. He is the recipient of the Ontario Early Researcher Award and the Sloan Fellowship, as well as the Outstanding Young Canadian Computer Scientist Award.

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.

Michelle started her group at the Université de Sherbrooke in 2011 where she is currently a full professor in the Department of Biochemistry and Functional Genomics and a member of the RiboClub. The characterization of the snoRNome has been the main focus of her group since its beginning, including elaborating diverse tools for the study of snoRNAs and the analysis their regulation, evolution, interactions and functions. Her group is also interested in studying different aspects of the transcriptome and its deregulation in health and disease. Her group is involved in collaborations with many groups. She is funded by CIHR, NSERC, FRQNT and holds a senior professorship from the FRQS.

Our group focuses on understanding the molecular events underlying the progression of early breast lesions. We often use different types of high-throughput profiling methods to provide comprehensive molecular overviews of the lesion, its microenvironment and the patient systemic response. We also develop new methods to subject cells and tissues to multiple genetic interventions simultaneously. The profiling information is used to build computational tools to understand higher-order interactions in complex biological systems and pathways. Our goal is to develop tools for end-points with clinical relevance and to use these tools to characterize events in tumoral progression.

Mira is a principal research officer and team led at the Digital Technologies RC and Adjunct Professor in Biochemistry, Microbiology and Immunology at University of Ottawa. Her main interests are in the application of ML and data mining life sciences with particular interest in metabolomics and lipidomics and applications in the diseases of aging and neurodegeneration. She was involved in the development of bioinformatics solutions (made available through https://complimet.ca) as well as utilization of computational biology for biomarker discovery and simulation of biological systems.

Mohamed is a computational systems biologist at the Vaccine and Infectious Disease Organization (VIDO). He obtained his undergraduate degree in Genetics from the School of Agriculture, Al-Azhar University (Cairo, Egypt) and a postgraduate diploma in Information Technology from the Information Technology Institute (ITI) (Giza, Egypt). Mohamed got his MSc and PhD degrees in Computational Systems Biology from Keio University (Tokyo, Japan). He completed his postdoctoral training in Bioinformatics at the School of Pharmaceutical Sciences, Kyoto University (Kyoto, Japan) and the School of Medicine, University of Toronto (Toronto, Canada).

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.

Nadia Tahiri received her M.Sc. and Ph.D. degrees in Computer Science from the University of Quebec at Montreal. She is currently an Assistant Professor in the Department of Computer Science at the University of Sherbrooke. Her research interests include evolution, phylogenetic tree, clustering, classification, computational biology, and biogeography, and consensus tree/supertree.

Nicholas Provart is a professor of plant cyberinfrastructure and systems biology and is chair of the Department of Cell & Systems Biology at the University of Toronto. Currently his Bio-Analytic Resource (BAR) at bar.utoronto.ca, comprising tools for coexpression analysis of publicly-available gene expression data, cis-element prediction, identifying molecular markers, generating “electronic fluorescent pictographic” (eFP) representations of gene expression patterns, and exploring protein-protein interactions in Arabidopsis and other plants, receives 4M page views a month by researchers worldwide. He is one of the founding members of the International Arabidopsis Informatics Consortium, is president of the Multinational Arabidopsis Steering Committee, and is teaching five MOOCs on bioinformatic methods, plant bioinformatics, and data visualization for genome biology on Coursera.org.

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.