Giovanni Palla
Research Scientist at scverse
Palo Alto, California, United States
Summary
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Rockstar🎓
Top SchoolGiovanni Palla is a machine learning researcher with eight years of experience at the intersection of computational biology and probabilistic ML, currently based in Palo Alto and working at Lila Sciences while co-founding the scverse open-source ecosystem. His background spans academic doctoral research in systems medicine and translational roles at organizations like the Chan Zuckerberg Initiative, Biohub, and Microsoft, where he translated single-cell genomics problems into deployable ML solutions. Giovanni contributes to foundational ML tooling—having refactored bijector code in the notable tensorflow/probability repository—demonstrating care for robust, production-ready probabilistic transformations. He brings hands-on experience with scRNA-seq analysis from industry internships and a Master’s in Drug Innovation, blending wet-lab insight with software engineering rigor. Colleagues value his ability to bridge open-source community leadership and research-driven product development to accelerate biological discovery.
7 years of coding experience
Master's degree, Drug Innovation, 9.15/10 cum laude, Master's degree, Drug Innovation, 9.15/10 cum laude at Utrecht UniversityBachelor's degree, Biotechnology, 110/110 cum laude, Bachelor's degree, Biotechnology, 110/110 cum laude at Università degli Studi di Trento
Github Skills (7)
probabilistic-programming10
tensorflow10
python10
machine-learning9
statistics7
deeplearning-ai7
deep-learning7
Programming languages (15)
Github contributions (5)
tensorflow/probability
Mar 2020 - Jul 2020
Probabilistic reasoning and statistical analysis in TensorFlow
Role in this project:
Back-end Developer
Back-end DeveloperContributions:7 commits, 3 PRs, 11 comments in 3 months
Contributions summary:Giovanni primarily focused on refactoring and updating code related to bijectors within the TensorFlow Probability library. Their work involved removing deprecated functionalities, fixing import statements, and adding log scale functionality to the `RealNVP` and `MaskedAutoregressiveFlow` bijectors. Furthermore, the user merged a branch and made small adjustments to code, including fixing linting issues. The commits demonstrate a focus on maintaining and improving the core functionality of the library's probabilistic transformations.
statisticspythonprobabilistic-reasoningdata-sciencedeep-learning
giovp/scanpy
Jan 2020 - Nov 2020
Single-Cell Analysis in Python. Scales to >1M cells.
Contributions:75 pushes, 17 branches in 9 months
cell-analysispythonnumbabioinformaticssingle-cell-analysis
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