Dan Gunter
Computer Systems Manager 1 at Berkeley Lab
Martinez, California, United States
Summary
👤
Senior🎓
Top SchoolDan Gunter is a seasoned computer scientist and systems manager with 18+ years at Lawrence Berkeley National Laboratory, focused on making distributed computing reliably serve scientific discovery. He combines deep hands‑on skills in Python, R, C and databases with expertise in TCP WAN tuning, middleware for data movement and workflow orchestration to optimize large-scale scientific pipelines. As a group lead and contributor to notable open-source projects like pymatgen and the Fireworks workflow, he improves core tooling and performance instrumentation for research communities. Dan balances technical delivery with research and proposal writing, translating complex requirements into robust, reusable middleware that helps scientists do better science. An early penchant for both low-level debugging and high-level workflow design gives him a rare ability to trace performance issues from network stack to analytics code.
17 years of coding experience
M.S., M.S. at San Francisco State UniversityBachelor of Arts, Bachelor of Arts at Brown UniversityThe Lawrenceville School
Github Skills (14)
jupyter-notebook10
pymatgen10
performance-monitoring10
science10
python10
materials-informatics10
workflow-management9
version-control9
etl9
git9
git-repository8
github-repos8
mongodb7
mongodb-database7
Programming languages (11)
Github contributions (5)
IDAES/idaes-pse
Oct 2018 - Sep 2022
The IDAES Process Systems Engineering Framework
Role in this project:
Backend Developer
Backend DeveloperContributions:188 reviews, 1129 commits, 61 PRs in 3 years 11 months
Contributions summary:Dan's commits focus on merging and integrating changes to a Jupyter Notebook file within the IDAES Process Systems Engineering Framework repository. Their work included merging branches and incorporating updates from a master branch, suggesting that the individual was involved in maintaining or contributing to the evolving code within the project. Furthermore, the user made several modifications to the notebook demonstrating a continuous process of improving and adapting the project's codebase. The user appears to be working on developing or integrating changes in the Module_2_Flowsheet workshop notebook.
chemical-engineeringengineeringelectrical-engineeringprocess-modelingenergy-system
materialsproject/pymatgen
Oct 2011 - Oct 2012
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
Back-end DeveloperContributions:29 commits in 11 months
Contributions summary:Dan primarily contributed to the `pymatgen` library, focusing on enhancements and bug fixes within the materials science domain. Their commits involved modifications to core functionalities, including workflow generation (`wf.py`), materials data extraction (`materials.py`), and integration with VASP input/output files. The contributions demonstrate a focus on the project's core functionality and its application to materials science workflows. The user also worked on the extraction, transformation, and loading (ETL) framework, as well as various test files.
moleculespythonscienceelectronic-structurepowers
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