Published multiple papers at conferences such as NeurIPS, ICML, CVPR, covering topics including trans-dimensional generative modeling via jump diffusion models, graphically structured diffusion models, visually chain-of-thought diffusion models, flexible diffusion modeling of long videos, conditional image generation by conditioning variational auto-encoders, planning as inference in epidemiological models, attention for inference compilation, near-optimal glimpse sequences for improved hard attention neural network training, and assisting the adversary to improve GAN training.
Research Experience
Interned at Google DeepMind.
Education
PhD: University of British Columbia, supervised by Frank Wood; MEng: Engineering Science at the University of Oxford.
Background
Research interests: generative modeling, especially with diffusion models. Applications in the video domain and for problems with explicit structural information.