Rishit Dagli

I also write a blog.

I am a CS Sophomore at The University of Toronto. I love researching and working with Machine Learning, especially Computer Vision. Coming from the software and robotics background, I contribute extensively to/ maintain popular open-source projects like TensorFlow, PyTorch Foundation, Kubernetes, Kubeflow, PapersWithCode, freeCodeCamp among others. I also love building open-source projects, some of which have been pretty popular which could be found on my GitHub. Seeing my work at a rather young age, I was invited to speak at 2 TEDx and 1 TED-Ed events. In a previous life I used to do a lot of robotics and software development. Furthermore, I have also represented my country in international olympiads. Feel free to talk with me about anything CS, Math, Robotics, or Physics.

I am currently working as a Research Intern at Qualcomm Research researching video models. I am advised by Prof. David Lindell as a part of the DGP Lab, Vector Institute at the University of Toronto where I research Diffusion.

My research community service can be summarized as being a: program committee member for ICLR Tiny Papers 2023; reviewer for ICLR PML4DC 2023, NeurIPS 2023, Cloud Native Wasm Day, and ICLR 2024.

As to why I got drawn to Computer Vision, I do believe it is one of the richest modalities but also read this excerpt when I was quite young, from the book, “Visual Reconstruction” by Andrew Blake and Andrew Zisserman, some of my favourite researchers:

We count it a great privilege to be working in a field as exciting as Vision. On the one hand there is all the satisfaction of making things that work - of specifying, in mathematical terms, processes that handle visual information and then using computers to bring that mathematics to life. On the other hand there is a sense of awe (when time permits) at the sheer intricacy of creation. Of course it is the Biological scientists who are right in there; but computational studies, in seeking to define Visual processes in mathematical language, have made it clear just how intrinsically complex must be the chain of events that constitutes “seeing something”.