The MAGPIE Group

Caroline Colijn

Caroline Colijn works at the interface of mathematics, evolution, infection and public health. She joined SFU's Mathematics Department in 2018 as a Canada 150 Research Chair in Mathematics for Infection, Evolution and Public Health. She did her PhD in applied mathematics at the University of Waterloo, where she studied the foundations of quantum mechanics. She changed tack in her postdoctoral years, working on mathematical modelling with Prof. Michael Mackey at McGill and on TB modelling and epidemiology in Megan Murray's group at the Harvard School of Public Health and the Broad Institute at MIT. She moved to the Department of Engineering Mathematics in Bristol, England in 2007 and joined Imperial College London's Department of Mathematics in 2011. She has broad interests in applications of mathematics to questions in evolution and public health, and was a founding member of Imperial's Centre for the Mathematics of Precision Healthcare. Contact

Maryam Hayati

Maryam is a Ph.D. student in the computing science department at Simon Fraser University. Her research area is computational biology, specifically phylogenetics, epidemiology, and evolution of pathogens.

Jason Spence

Jason is an undergraduate majoring in the Biological Sciences. He is particularly interested in evolution, which has lead him to pursue a minor in Computing Science, and research in a Mathematics lab, to better explore its complexity. Jason is an active participant in graduate-level activities around the Biology department, and spends his extra time as a student union executive and participating in music clubs.

Jessica Stockdale

Jessica is a post doc with interests in mathematical and statistical modelling of infectious disease outbreaks, particularly using genomic data. Before moving to SFU in October 2018, she completed her PhD at the University of Nottingham, UK, working on Bayesian computational methods for stochastic epidemics. Her current projects focus on modelling of pathogen genomic data to better understand transmission and evolution, and predict future infection.

Priscila Do Nascimento Biller

Priscila received her Bachelor, Master, and Ph.D. in Computer Science from the University of Campinas, Brazil. She has been working as a computational biologist, building on mathematical concepts to develop new models geared towards understanding natural evolution phenomena. During her Ph.D. she studied statistical and combinatorial problems related to the evolution by genome rearrangements, advised by Dr. João Meidanis. Later she did a postdoc at INRIA Grenoble Rhône-Alpes, France, under the supervision of Dr. Eric Tannier, working on an experimental validation of computational evolvability, a framework derived from machine learning with the purpose of providing a theoretical assessment of the time-scale required to evolve a certain class of functions. Currently she is interested in birth–death processes for generating phylogenetic trees with a potential application in the context of evolution of pathogens.

Pengyu (Peter) Liu

Pengyu is a post doc whose research interests lie in the fields of topology, combinatorics and their applications in biology, physics and other sciences. Current projects include tree metrics and topology and their applications in phylogenetics and infectious diseases, knots and links and their invariants and the topology of the kinetoplast DNA networks.

Nicola Mulberry

Nicola is a PhD student in the Applied Mathematics program at SFU. Her interests are in mathematical modelling and computing with applications to public health. She is currently interested in the evolution of co-circulating pathogens. Nicola is also involved in an ongoing project to develop and promote open educational resources for undergraduate math students.

Kurnia Susvitasari

Kurnia is a PhD student in the Department of Mathematics, currently enrolled in the Applied Mathematics program. Her research interest is in statistical methods to understand infectious disease outbreaks using genomic data. She is currently working to understand cluster outbreaks using logistic regression.