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@for-ai @VectorInstitute @UTMIST
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I research generative models and LLMs in collaboration with @Google-Brain and @StanfordAI. I'm a recent graduate of CS, Math, and Stats at the University of Toronto where I worked on latent variable models, probabilistic inference, and neural ODEs/SDE at the Vector Institute. I'm seeking PhD oppys for 2023.

Previously, I scaled Transfomers on human-generated content at and developed deep learning tools for robotics @NVIDIA. Like my models, I'm still learning (:


  1. bayeSDE Public

    Code for "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations"

    Python 124 20

  2. Self-Similarity Priors: Neural Collages as Differentiable Fractal Representations

    Jupyter Notebook 11

  3. Python library which enables complex compositions of language models such as scratchpads, chain of thought, tool use, selection-inference, and more.

    Python 80 6

  4. Fitting a recognition model (VAE) to do approximate inference on intractable posteriors of probabilistic models using an ELBO estimator.

    Python 7 1

  5. Generating structured sparsity in neural networks using weight and unit pruning techniques.

    Python 1

  6. Predicting transcription factor binding sites using next-generation sequencing genomic data and Segway.


588 contributions in the last year

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Mon Wed Fri
Activity overview
Contributed to xwinxu/, xwinxu/dotfiles, erichson/NFM and 11 other repositories

Contribution activity

September 2022

11 contributions in private repositories Sep 1 – Sep 23

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