Rishit Dagli

2nd year CS Undergrad @ UofT, ML and Vision Research @ Civo, DGP Lab, Vector Institute


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 (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. 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 advised by Prof. David Lindell as a part of the DGP Lab, Vector Institute at the University of Toronto where I research Diffusion. I am currently working as a Research Intern at Civo Cloud researching vision and multimodal models where I am advised by Josh Mesout.

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”.

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


Feb 13, 2024 Received the T-CAIREM award for students at UofT.
Aug 17, 2023 1 oral + 1 poster accepted to PyTorch Conference.
Mar 15, 2023 Soon joining Civo, a startup as one of the first ML research scientists.
Mar 2, 2023 1 paper accepted to ICLRW.
Jan 17, 2023 Recipient of the Google AI Research Grant for 2023.

selected publications

  1. masa.png
    Tuning In custom_emoji : Analysis of Audio Classifier Performance in Clinical Settings with Limited Data
    Hamza MahdiEptehal NashnoushRami Saab, and 4 more authors
    Feb 2024
  2. diffuseraw.jpg
    DiffuseRAW: End-to-End Generative RAW Image Processing for Low-Light Images
    Rishit Dagli
    Feb 2023
  3. truck_mnerf.png
    PyTorch Made Efficient for the Edge: WASI-NN
    Rishit Dagli, and Shivay Lamba
    In PyTorch Conference, Oct 2023
  4. wasm_akri.png
    Orchestrating Machine Learning on Edge Devices with PyTorch and WebAssembly (Oral)
    Rishit Dagli, and Shivay Lamba
    In PyTorch Conference, Oct 2023
  5. astroformer.png
    Astroformer: More Data Might Not be All You Need for Classification
    Rishit Dagli
    In International Conference on Learning Representations Workshops, Apr 2023
  6. cppe5.png
    CPPE-5: Medical Personal Protective Equipment Dataset
    Rishit Dagli, and Ali Mustufa Shaikh
    SN Computer Science, Mar 2023
  7. q.png
    Deploying a smart queuing system on edge with Intel OpenVINO toolkit
    Rishit Dagli, and Süleyman Eken
    Soft Computing, Aug 2021