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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.

Pinned

  1. easy-neural-ode Public

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

    Python 229 28

  2. google/jax Public

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

    Python 20.3k 1.9k

  3. wgrathwohl/VERA Public

    Python 54 9

  4. gibbs-jem Public

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

    Python

  5. slurm Public

    Scripts for launching sweeps on a SLURM cluster.

    Python

  6. sequencing Public

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

    C++ 2

2,400 contributions in the last year

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Contribution activity

September 2022

Opened 1 pull request in 1 repository

Created an issue in google/jax that received 5 comments

scatter_mul autodiff bug

Description Code: import numpy as np from jax import lax from functools import partial import jax import jax.numpy as jnp from jax.tree_util import…

5 comments
1 contribution in private repositories Sep 22