Junru Lin

I’m a third-year undergraduate studying Computer Science, Statistics, and Mathematics at the University of Toronto. I’m broadly interested in computer vision and machine learning, and particularly in 3D reconstruction, probabilistic modeling, and deep learning. I’m currently working with David B. Lindell and Igor Gilitschenski on NeRF.

Previously, I was fortunate to work with Kyros Kutulakos on computational imaging. I also did some research with Lisa Jeffrey and Carolyn Sealfon, and worked as a web developer with David Liu.

In my spare time, I enjoy playing badminton and reading.

Feel free to get in touch if you’d like to chat: junru.lin@mail.utoronto.ca.

news

Dec 31, 2022	My website is updated! ✨
May 1, 2022	I start my internship at the Dynamic Graphics Project Lab!
Sep 1, 2020	I start my undergrad at the University of Toronto! 🎓

selected publication

SIURO
Rapid Testing in COVID and Modified SIR Model

Chen, J.*, Li, R.* and Lin, J.*

SIAM Undergraduate Research Online, 2022

Abs Web PDF Bib

The COIVD pandemic has swept the globe since 2019, posing a grave threat to human life. There are multiple ways for the government to control the pandemic, including promoting the vaccination, limiting the number of people in public places, requiring people to wear masks in public places, and suggesting infected people isolate themselves. In this paper, we used a compartmental model to analyze the spread of COVID-19 under the promotion of rapid tests. The result shows that popularization of rapid tests may have a significant impact on controlling the pandemic. With an estimated minimum requirement for the use of rapid tests, we are able to put forward suggestions on reasonable ways to curtail the pandemic.
@article{covid, title = {Rapid Testing in COVID and Modified SIR Model}, author = {{Chen, J.*, Li, R.* and Lin, J.*}}, journal = {SIAM Undergraduate Research Online,}, volume = {15}, year = {2022}, doi = {10.1137/21S1460399}, publisher = {Society for Industrial and Applied Mathematics,}, }