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 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.
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.
Tuning In : Analysis of Audio Classifier Performance in Clinical Settings with Limited DataFeb 2024
DiffuseRAW: End-to-End Generative RAW Image Processing for Low-Light ImagesJan 2024
PyTorch Made Efficient for the Edge: WASI-NNIn PyTorch Conference, Oct 2023
Orchestrating Machine Learning on Edge Devices with PyTorch and WebAssembly (Oral)In PyTorch Conference, Oct 2023
Astroformer: More Data Might Not be All You Need for ClassificationIn International Conference on Learning Representations Workshops, Apr 2023
CPPE-5: Medical Personal Protective Equipment DatasetSN Computer Science, Mar 2023
Deploying a smart queuing system on edge with Intel OpenVINO toolkitSoft Computing, Aug 2021
Job Descriptions Keyword Extraction using Attention based Deep Learning Models with BERTIn 2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), Jun 2021
Fast TransformerSep 2021
Gradient CentralizationMar 2021