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

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

    Python 266 31

  2. google/jax google/jax Public

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

    Python 29.3k 2.7k

  3. wgrathwohl/VERA wgrathwohl/VERA Public

    Python 62 10

  4. 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

  5. slurm slurm Public

    Scripts for launching sweeps on a SLURM cluster.

    Python

  6. sequencing sequencing Public

    Fast alignment of genomic sequences using Boyer-Moore with linear time construction of indexes using Z algorithm.

    C++ 2