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Javad Rajabi
I am a Computer Science PhD student at the University of Toronto, co-supervised by David B. Lindell and Babak Taati, and a Faculty Affiliate Researcher at the Vector Institute. I am also a research intern at Samsung AI Center Toronto.
My research focuses on scaling diffusion and flow-matching models for image and video generation, with an emphasis on training-free methods that unlock capacity already present in pretrained models. I work along three axes: quality, designing new guidance methods that steer sampling toward better outputs; space, generating at resolutions well beyond what models were trained on while keeping compute in check; and time, accelerating the many-step sampling process so that high-quality image and video generation becomes fast and affordable.
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