Gökçen Eraslan
Principal AI Scientist at Genentech
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolGökçen Eraslan is a Principal AI Scientist in San Francisco with 16 years of experience bridging computational biology and machine learning, and over seven years focused on genomics-driven ML research. Currently leading AI efforts at Genentech after postdoctoral roles at Broad Institute and academic training culminating in a PhD from Technical University Munich, she combines deep domain knowledge in epigenetics with production-grade ML engineering. Her open-source contributions include enhancements to the widely used anndata project for single-cell data and TensorBoard integration work in Keras, reflecting an ability to improve both scientific data pipelines and developer tooling. Known for translating complex biological questions into scalable, testable software, she brings a rare blend of research rigor and practical software craftsmanship to interdisciplinary teams.
16 years of coding experience
9 years of employment as a software developer
Master of Science - MS, Computational Biology, Master of Science - MS, Computational Biology at Aalto UniversityMaster of Science - MS, Systems Biology, Master of Science - MS, Systems Biology at KTH Royal Institute of TechnologyDoctor of Philosophy - PhD, Doctor of Philosophy - PhD at Technical University MunichBS, Computer Engineering, BS, Computer Engineering at Anadolu Üniversitesi
English, Turkish
Stackoverflow
1,390reputation
286kreached
13answers
6questions
pdf
top-5%
Github Skills (27)
python10
artificial-neural-networks10
data-science10
pandas10
machine-learning10
numpy10
keras10
deeplearning-ai10
deep-learning10
tensorflow10
fileio10
file-processing10
file-handling10
neural-network10
bioinformatics10
Programming languages (18)
Github contributions (5)
scverse/anndata
May 2018 - Mar 2022
Annotated data.
Role in this project:
Data Scientist
Data ScientistContributions:4 reviews, 17 commits, 9 PRs in 3 years 10 months
Contributions summary:Gökçen made several contributions to the `anndata` repository, primarily focused on enhancing its data handling capabilities. These contributions include adding support for reading and writing data from compressed text files (gz, bz2), integrating functionality for reading and writing loom files, and modifying the code to incorporate additional arguments for loompy connect, including options for writing obsm and varm attributes. Furthermore, the user also added a test for the layer deepcopy and updated the release notes to reflect recent changes.
datascversedata-sciencescanpymachine-learning
keras-team/keras
May 2017 - Jul 2017
Deep Learning for humans
Role in this project:ML Engineer
ML EngineerContributions:6 commits, 8 PRs, 86 comments in 2 months
Contributions summary:Gökçen primarily contributed to the TensorBoard integration within the Keras deep learning framework. Their work involved adding features for visualizing gradients, embedding layers, and fixing related issues. They also implemented tests for these features, ensuring proper functionality across different model types and configurations. Additionally, the user addressed documentation issues related to TensorBoard usage.
pythondata-sciencedeep-learningneural-networksmachine-learning
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