Jesse Knight

Jesse Knight (NSERC PhD Student) LinkedInORCID Google ScholarGitHub-Mark-64px
Jesse is a PhD student working with Sharmistha (Sept 2019, NSERC CGS-D). Jesse first joined the team in April 2018 as research staff (mathematical modeler and data scientist). He was awarded the NSERC Doctoral Award for his PhD studies Jesse comes from a biomedical engineering background (MASc 2017, BEng 2015; University of Guelph). His previous work focused on automated segmentation of brain MRI and analysis of digital histopathology.

Jesse enjoys developing new mathematical models of epidemiologic phenomena and incorporating them into deterministic compartmental models of STI transmission. His recent work explored how turnover of individuals between risk groups could influence the projected importance of reaching key populations with care. He presented his work on turnover at 2019 International Society for Sexually Transmitted Diseases Research (oral presentation) and his modeling of the consequence of unmet HIV treatment needs of key populations at Conference on Retroviruses and Opportunistic Infections 2019. He is also interested in complex mixing patterns in STI transmission models, and advanced techniques for model fitting. Jesse’s PhD research will explore how differences in model structure can influence epidemic projections of HIV in Southern Africa, with a focus on modeling of key populations.

Jesse has made several unsuccessful attempts to fully automate his life using bash scripting. In his spare time, Jesse likes to: go backcountry camping, pester you to try LaTeX, and worry about the climate crisis.

Jesse’s turnover paper is now in published:

Jesse’s other papers with the team include:

  • Wang L, Moqueet N, Simkin A, Knight J, Ma H, Lachowsky NJ, Armstrong HL, Tan DHS, Burchell AN, Hart TA, Moore DM, Adam BD, MacFadden DR, Baral S, Mishra S. (2021). Mathematical modelling of the influence of serosorting on the population-level HIV transmission impact of pre-exposure prophylaxis. AIDS. Accepted 11 Jan 2021.