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jacobjinkelly/README.md

I'm a Research Engineer at DeepMind. I completed my undergrad in Computer Science, Math, and Stats at the University of Toronto, where I was fortunate to work with Roger Grosse and David Duvenaud at the Vector Institute. My goal is to use machine learning to understand biology. I'm interested in energy-based models, latent variable models, neural ODEs, and genomics.

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  1. google-deepmind/alphafold3 google-deepmind/alphafold3 Public

    AlphaFold 3 inference pipeline.

    Python 4.7k 493

  2. jax-ml/jax jax-ml/jax Public

    Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

    Python 30.5k 2.8k

  3. easy-neural-ode easy-neural-ode Public

    Code for the paper "Learning Differential Equations that are Easy to Solve"

    Python 269 31

  4. wgrathwohl/VERA wgrathwohl/VERA Public

    Python 62 10

  5. gibbs-jem gibbs-jem Public

    Code for the paper "Directly Training Joint Energy-Based Models for Conditional Synthesis and Calibrated Prediction of Multi-Attribute Data"

    Python 1 1

  6. slurm slurm Public

    Scripts for launching sweeps on a SLURM cluster.

    Python