Victoria V.

Mathematics PhD @ UBC

Vancouver, British Columbia, Canada

About

https://vvsection.com

Experience

  • Assistant Scientist at HTuO Biosciences
    May 2026 - Present · 3 mos

  • University of Toronto (2 yrs 9 mos)
    • Teaching Assistant
      Aug 2022 - Apr 2025 · 2 yrs 9 mos

      MAT135, MAT136, MAT232, MAT236, STA107 Teaching Assistant Lecturing, conducting office hours, preparing class materials/tutorials/tests/exams, grading assignments/exams, communicating with TAs.

    • Research Student
      May 2023 - Aug 2023 · 4 mos

      Study of Multi-Particle Diffusion Limited Aggregation (MDLA) - stochastic model of infection spread. This project compiled the tools needed to understand the model, such as the foundations of measure-theoretic probability, and culminated in a proof of a theorem concerning the growth of MDLA in a certain space. Supervised by Prof. Duncan Dauvergne.

  • Research Assistant at Princess Margaret Cancer Centre
    May 2024 - Aug 2024 · 4 mos

    • Explored various biophysical and genetic features of low-complexity regions in the human proteome • Established prediction biases for proteins with low-complexity regions in AlphaFold and ProstT5 (Foldseek) protein language models • Traced the differences that alternative splicing induces in predicted protein structures with regards to low-complexity regions • Identified that cancer mutations are significantly less likely to happen on low-complexity regions • Created data analysis pipelines for processing genomic information from gnomAD and The Cancer Genomic Atlas

  • Volunteer Research Assistant at University of Toronto, Rauscher Lab
    May 2021 - Sep 2022 · 1 yr 5 mos

    Conceptualizing and testing dimensionality reduction methods for protein simulation data to increase interpretability. Performing quantitative and qualitative analyses of a dimensionality reduction method based on variational autoencoders. Building Markov state models of protein simulations to discover the underlying dynamics. Utilizing the University’s high-performance computing resources (Niagara and MIST) to train neural networks and run extensive protein simulations.