Nathan Frey - CTO at Guide Labs

Nathan Frey

CTO at Guide Labs

New York, New York, United States

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Summary

🤩

Rockstar

🎓

Top School

Nathan Frey is an interdisciplinary AI and life sciences leader with ~10 years of experience building foundation models and deploying ML-driven biomolecule design in industry, most recently as Co-founder & CTO of Coefficient Bio (acquired by Anthropic) and now leading Life Sciences efforts at Anthropic. He has run multidisciplinary teams at Genentech/Prescient Design to set AI strategy and product roadmaps, established an industry-facing NVIDIA collaboration, and translated generative modeling advances into drug discovery programs. A prolific researcher with 20+ publications and an ICLR Outstanding Paper Award, he also contributes to major open-source projects in computational chemistry and materials science such as DeepChem, atomate, and pymatgen. Nathan’s background spans hands-on model engineering, materials physics (PhD, UPenn), and postdoctoral supercomputing work at MIT—an unusual blend that helps him bridge scalable ML infrastructure with experimental biology.

9 years of coding experience

14 years of employment as a software developer

Master’s Degree Physics, Master’s Degree Physics at Boston University

University of Missouri

Doctor of Philosophy (Ph.D.) Materials Science, Doctor of Philosophy (Ph.D.) Materials Science at University of Pennsylvania

Japanese

Github Skills (10)

machine-learning10

pymatgen10

tensorflow10

science10

python10

data-science10

materials-informatics10

computational-materials-science10

data-analysis10

pandas9

Programming languages (4)

CHTMLJupyter NotebookPython

Github contributions (5)

deepchem/deepchem

Jun 2020 - Dec 2021

Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology

Role in this project:

Data Scientist & ML Engineer

Contributions:42 reviews, 134 commits, 52 PRs in 1 year 6 months

Contributions summary:Nathan primarily contributed to a tutorial demonstrating the training of a Normalizing Flow (NF) model on the QM9 dataset. The commits show changes within an example tutorial, including the installation of necessary dependencies. The user also worked on molecular data processing and likely model training within the tutorial.

chemistrysciencedrug-discoverydeep-learningbiology

materialsproject/pymatgen

Jun 2019 - Mar 2020

Python Materials Genomics (pymatgen) is a robust materials analysis code that defines classes for structures and molecules with support for many electronic structure codes. It powers the Materials Project.

Role in this project:

Back-end Developer & Data Scientist

Contributions:47 commits, 2 PRs, 1 comment in 9 months

Contributions summary:Nathan primarily contributed to the `pymatgen` repository by implementing and refining functions within the `heisenberg.py` module, which appears to be focused on materials analysis and modeling. Their work involved creating and updating functions related to the Heisenberg model, including the calculation of exchange parameters and the estimation of critical temperatures. The commits demonstrate the development of tools for analyzing magnetic orderings in materials science.

moleculespythonscienceelectronic-structurepowers

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