Dr. Caroline Colijn
Dr. Colijn’s work is at the interface of mathematics and the epidemiology and evolution of pathogens. She holds a Canada 150 Research Chair in Mathematics at Simon Fraser University for Evolution, Infection and Public Health.
She leads the Mathematics, Genomics and Prediction in Infection and Evolution (MAGPIE) research group, which, alongside their own research, provides public health support with pandemic modelling. Their work involves case-forecasting, vaccination parameter estimation, genomic epidemiology and other topics in relation to COVID-19. Professor Colijn is the co-lead of the new Canadian Network for Modelling Infectious Disease (CANMOD). This collaboration between Public Health Agency of Canada and NSERC increases Canada’s capacity for infectious disease modelling to directly support short, medium, and long-term public health decisions. It builds and coordinates national capacity by sharing research problems, models and estimates, data files and expertise. Throughout the pandemic, she has advised and collaborated with government and public health institutions about COVID-19. In 2020, Professor Colijn, was announced as a recipient of the Radio Canada Scientist of the Year prize for 2020 alongside three other mathematicians for her work to model the impact of physical distancing on the infection curve in British Columbia.
Professor Colijn develops mathematical tools connecting sequence data to the ecology and evolution of infections. She also has a long-standing interest on the dynamics of diverse interacting pathogens. For example, how does the interplay between co-infection, competition and selection drive the development of antimicrobial resistance? To answer these questions, her group is building new approaches to analyzing and comparing phylogenetic trees derived from sequence data, studying tree space and branching processes, and developing ecological and epidemiological models with diversity in mind. She is a founding member of Imperial College London’s Centre for the Mathematics of Precision Healthcare. She received her PhD in mathematics from the University of Waterloo.
Dr. Benjamin Haibe-Kains
Biography: Trained as a computer scientist, Dr. Benjamin Haibe-Kains earned his PhD in Bioinformatics at the Université Libre de Bruxelles (Belgium). He was a postdoc in the Quackenbush group at the Dana-farber Cancer Institute and Harvard School of Public Health (USA). Dr. Haibe-Kains started his own laboratory at the Institut de Recherches Cliniques de Montréal (Canada) and he is now Principal Investigator at the Princess Margaret Cancer Centre. His research focuses on the integration of high-throughput data from various sources to simultaneously analyze multiple facets of diseases, with a particular emphasis on cancer. Dr. Haibe-Kains and his team are using publicly available genomic datasets and data generated through his collaborations to better understand the biology underlying carcinogenesis and to develop new predictive models in order to significantly improve disease management. Dr. Haibe-Kains' main scientific contributions include several prognostic gene signatures in breast cancer, subtype classification models for ovarian and breast cancers, as well as genomic predictors of drug response in cancer cell lines. Dr. Haibe-Kains has published more than 150 peer reviewed publications with a high citation impact of 23830 citations.
Dr. Luke Bornn
Dr. Luke Bornn is recognized as a world leader in sports analytics, and is Co-Founder and Chief Scientist at Zelus Analytics, a world-leading sports analytics company providing sports intelligence to professional teams. Dr. Bornn was Vice President, Strategy and Analytics for the NBA Sacramento Kings and served as Head of Analytics for A.S. Roma of the Italian Serie A Football League, where he worked closely with managers, coaches and sports scientists to measure and evaluate athletes and performance. In addition to his work with soccer and basketball teams, the British Columbia native has previously held tenure-track professorships in Statistics at both Harvard University and Simon Fraser University. Bornn is a frequent contributor to the field of sports analytics, authoring research articles for the Journal for Quantitative Analysis, the Annals of Applied Statistics and the Journal of the American Statistical Association amongst others. His academic research is focused on developing statistics and machine learning methods for high dimensional spatio-temporal data, with a primary focus on extracting insights from player tracking data in sports. He was a finalist for the MIT SSAC research awards from 2014 through 2019, receiving the award in 2015 and 2019. He received his M.S. and Ph.D. in Statistics from the University of British Columbia.
Dr. Nithum Thain
Dr. Nithum (Nith) Thain is a Senior Research Engineer for Google and is recognized as an expert in Artificial Intelligence. Dr Thain holds numerous degrees in Mathematics from Queen’s University (BSc) and McGill (MSc, PhD), along with an MBA from Oxford as a Rhodes Scholar. He completed a postdoctoral fellowship at Simon Fraser University, where he worked on cutting-edge algorithms to quickly classify tuberculosis strains using minimal genetic data. He subsequently joined Jigsaw, a unit within Google that explores threats to open societies, and builds technology that inspires scalable solutions. There, he did research on using natural language processing techniques in machine learning to help tackle online abuse. At Google Brain, he has worked on issues of AI Responsibility and Fairness. He has also taught Natural Language Processing with Deep Learning at the UC Berkeley School of Information and has advised numerous start-up companies on machine learning and data science approaches to assist with business decision-making and analytics. Since leaving Oxford, Nithum has done all of this while mainly residing in Newfoundland, the province where he grew up. Thain is a frequent contributor to the field of Machine Learning, Natural Language Processing, Artificial Intelligence, and Game Theory, authoring numerous articles, including proceedings in the International Conference on World Wide Web and the Association for Computing Machinery Conference on AI, Ethics, and Society.