This post describes the notation I’ll use elsewhere to discuss differential equation problems.
I am a PhD student in modelling and computational science under the supervision of Dr. Hendrick de Haan in the cNAB.LAB for computational nanobiophysics. Recently, I’ve been interested in using deep neural networks to solve the partial differential equations that describe electric fields and molecular transport through nanofluidic devices. I’ve also been using these mathematical problems as a controlled setting in which to study deep neural networks themselves. For instance, by studying the internal structure of deep neural networks trained to solve a parametrized family of boundary value problems, I learned that these networks can learn general representations that are robust even across variations in the problem parameters.
Previously, I conducted research across a broad range of areas in computational science and data analysis. As part of an international collaboration studying nanoscale preconfinement of DNA molecules translocating through a nanopore, I modelled and simulated molecules in the device, and assisted with the analysis of experimental data. That work was published as the cover article of Nano Letters. In my master’s thesis, I studied the translocation of polymers through a nanofluidic device consisting of two nanopores connected by a nanocavity. This work was published in Physical Review Letters and led to a patent. During my undergraduate degree, I conducted research in nuclear reactor physics at AECL, dark matter detection at SNOLAB, machine learning for cosmic ray tomography, and fluid dynamics under the supervision of Prof. Marek Stastna.
You can download my detailed CV here.MSc in Modelling and Computational Science, 2016
University of Ontario Institute of Technology
BMath in Applied Mathematics with Physics Option, 2014
University of Waterloo
This post describes the notation I’ll use elsewhere to discuss differential equation problems.
This post contains a brief overview on solving PDE problems directly using neural networks, including an overview of relevant literature.
Since 2017, I have been working at the UOIT Science Café. This is a weekly drop-in session open to all UOIT science students. I provide help with mathematics, physics, and computer science courses.
I also work as a teaching assistant. This entails reviewing lecture material in tutorials, providing focused help in office hours, and assisting with the administration and marking of assessments. I have been a teaching assistant for the following courses at UOIT:
I’ve also had the opportunity to present lectures on the following topics: