Carlos Hernández
Research Scientist at Meta
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolCarlos Hernández is a Research Scientist at Meta Reality Labs with 11 years of experience applying machine learning and biophysics to real-world products, following research roles at CTRL-labs. He holds a PhD in Biophysics from Stanford and a BS in Applied Mathematics from Columbia, blending rigorous theoretical training with practical systems engineering. At Meta he focuses on ML-driven research for AR/VR experiences, and his background includes backend and DevOps contributions to the popular MDTraj molecular dynamics library, where he improved trajectory parsing, packaging, and deployment. Known for bridging research and production, he iterates quickly from prototypes to reproducible releases and documentation. Based in New York, he brings a cross-disciplinary outlook that pairs computational rigor with hands-on infrastructure skills.
11 years of coding experience
1 year of employment as a software developer
Bachelor of Science (BS), Applied Mathematics, Bachelor of Science (BS), Applied Mathematics at Columbia University in the City of New YorkDoctor of Philosophy (PhD), Biophysics, Doctor of Philosophy (PhD), Biophysics at Stanford University
Spanish, English
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (8)
dynamics10
devops9
molecular-dynamics-simulation9
molecular-simulation9
debug9
release-management8
travis-ci7
python7
Programming languages (6)
Github contributions (5)
mdtraj/mdtraj
Jun 2015 - Sep 2018
An open library for the analysis of molecular dynamics trajectories
Role in this project:
Back-end & DevOps Engineer
Back-end & DevOps EngineerContributions:1 release, 13 commits, 24 PRs in 3 years 3 months
Contributions summary:Carlos contributed to both the back-end functionality and infrastructure of the MDTraj project. Their work included bug fixes and improvements to the core functionality of the library related to trajectory parsing and conversion. They also updated the build and deployment process by modifying the install script for Miniconda, demonstrating DevOps skills. Additionally, they released a new version of the library and updated documentation files.
pythonmdtrajmolecular-dynamicspdbpdb-files
cxhernandez/osprey
Mar 2016 - Apr 2019
osprey is the plumbing for machine learning hyperparameter optimization.
Contributions:119 pushes, 85 branches in 3 years 1 month
hyperparameteroptimizationhyperparametersmachine-learningosprey
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