Rishit Dagli

I also maintain rishit.tech and this is a smaller version of it.

I am a CS Freshman 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, Kubernetes, Kubeflow, PapersWithCode, freeCodeCamp among others. I also love building open-source projects (usually related to Kubernetes and Machine Learning), 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. 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 advised by Prof. David Lindell as a part of the DGP Lab at the University of Toronto where I research Diffusion. I am currently working as a Research Intern at SpaceX, where I am researching Computer Vision and Machine Learning before which I held the same position on NASA JWST (Exoplanet Detection Proposal) before which I have also been a research intern at Google AI under a grant.

My research community service can be summarized as being a Program Committee member for ICLR Tiny Papers 2023. Reviewer for ICLR 2023, ICLR PML4DC 2023, CVPR 2023, ICCV 2023, and NeurIPS 2023.

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”.

I deeply thank the following organization for current/ in the past supporting my research either through scholarships or research grants: Linux Foundation, Google AI, Google Cloud, University of Toronto, Vector Institute, Stanford, CNCF, and Intel AI.