Galen Xing
Ph.D. Candidate
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolGalen Xing is a Ph.D. candidate in Computational Biology at UC Berkeley with a decade of experience at the intersection of machine learning, single-cell genomics, and computational genetics. He has built and contributed to production-grade open-source tools for single-cell and spatial omics (notably scvi-tools) and extended foundational ML libraries like DeepChem with custom model evaluation and Sluice Network layers. His background spans academic research labs and industry R&D—from Chan Zuckerberg Biohub and Samsung AI to venture diligence roles—giving him a rare mix of deep technical expertise and startup/VC-facing product sense. Based in San Francisco, he combines hands-on engineering in probabilistic modeling and CRISPR-related computation with practical experience in sourcing and thesis-building for biotech investments. An interesting facet: he’s contributed targeted fixes and feature additions that improved data handling and testing in widely used bioinformatics and ML repositories, reflecting both attention to detail and impact on community tooling.
10 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy - PhD Computational Biology, Doctor of Philosophy - PhD Computational Biology at University of California, BerkeleyHigh School, High School at The Bronx High School of ScienceBachelor’s Degree Dual Degree in CS and Statistics, Bachelor’s Degree Dual Degree in CS and Statistics at Columbia University
English, Chinese, German
Github Skills (14)
rna-seq10
seq10
machine-learning10
single-cell-genomics10
deep-learning10
tensorflow10
python10
sc10
data-analysis10
pytorch9
drug-discovery8
biology8
science7
quantum-chemistry7
Programming languages (7)
Github contributions (5)
scverse/scvi-tools
Feb 2020 - Oct 2021
Deep probabilistic analysis of single-cell and spatial omics data
Role in this project:
Data Scientist
Data ScientistContributions:56 reviews, 654 commits, 89 PRs in 1 year 7 months
Contributions summary:Galen's commits primarily focused on adding functionality to process and analyze single-cell and spatial omics data within the scvi-tools repository. Their contributions included the addition of a `use_raw` argument and associated testing within the anndataset.py, extract_data_from_anndata() and test_anndataset.py files. They also made modifications to existing functionality and addressed warnings concerning incorrect datatypes.
hierarchicaldeep-generative-modelmixture-of-expertssingle-cell-rna-seqsingle-cell
deepchem/deepchem
Jul 2017 - Sep 2017
Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
Role in this project:ML Engineer
ML EngineerContributions:44 commits, 7 PRs, 5 comments in 1 month
Contributions summary:Galen primarily focused on modifying and extending the functionality of the `deepchem` library, specifically related to model evaluation and the `TensorGraph` module. Their contributions include adding arguments to the `compute_metric` function within the evaluation utilities and adding and modifying the `summary` operation within the `TensorGraph` layer. Furthermore, the user made significant changes, particularly through the implementation of Sluice Network layers and related unit tests.
chemistrysciencedrug-discoverydeep-learningbiology
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