Ekin Cubuk - Co-Founder at Periodic Labs

Ekin Cubuk

Co-Founder at Periodic Labs

Mountain View, California, United States

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Summary

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Rockstar

🎓

Top School

Ekin Cubuk is a physicist-turned-founder and research scientist with seven years of industry experience and a PhD in Condensed Matter and Materials Physics from Harvard. Based in Mountain View, he spent nearly eight years at Google DeepMind applying computational and machine learning techniques to scientific problems before co-founding Periodic Labs. His work bridges rigorous numerical methods and ML—contributing EAM potential implementations and Behler-Parrinello network support to the jax-md project, highlighting expertise in differentiable molecular dynamics and hardware-accelerated simulations. Ekin’s background blends postdoctoral research at Stanford with deep practical experience in production research systems, making him adept at turning complex physical models into scalable software. He brings a rare combination of academic depth, engineering discipline, and startup appetite for building applied science products.

6 years of coding experience

8 years of employment as a software developer

Doctor of Philosophy (PhD) Condensed Matter and Materials Physics, Doctor of Philosophy (PhD) Condensed Matter and Materials Physics at Harvard University

Bachelor of Arts (B.A.) Physics, Bachelor of Arts (B.A.) Physics at Swarthmore College

High School, High School at Robert College

Github Skills (8)

jax10

molecular-dynamics-simulation10

molecular-simulation10

python10

nonlinear-dynamics10

scientific-computing10

numpy10

machine-learning9

Programming languages (2)

Jupyter NotebookPython

Github contributions (4)

jax-md/jax-md

May 2019 - Oct 2021

Differentiable, Hardware Accelerated, Molecular Dynamics

Role in this project:

Back-end Developer / ML Engineer

Contributions:1 review, 36 commits, 38 PRs in 2 years 5 months

Contributions summary:Ekin focused on adding and testing EAM (Embedded Atom Model) potential functionality, a core component related to molecular dynamics simulations. Their work involved implementing the EAM potential, including features like loading parameters from LAMMPS files, and incorporating dtype testing. This suggests a focus on developing and testing numerical methods for the jax-md library. The user also contributed to a Behler-Parrinello network, adding to machine learning capabilities.

differentiablemolecular-dynamicsautomatic-differentiationacceleratedsimulation

ekindogus/BIG-bench

Jun 2021 - Jun 2021

Beyond the Imitation Game collaborative benchmark for enormous language models

Contributions:7 pushes, 1 branch in 28 days

benchmarkcollaborativelanguage-modelsbeyond

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