Alex Wolf is a founder and CEO with a decade of experience building open data infrastructure for biology, currently leading lamin.ai to provide context and memory for datasets and models at scale. He combines deep academic training in computational physics (PhD) with hands-on engineering, having created Scanpy and co-created the widely used anndata library that underpins single-cell analysis in Python. As an early engineering leader at Cellarity he architected the compute platform that supported its rapid growth and fundraising, and continues to advise biotech startups like Retro Biosciences. Active in the scverse consortium, he helps align and steward critical open-source tools for the life sciences community. Alex’s work spans research, production systems, and publications, uniquely bridging many-body physics techniques and scalable bioinformatics. He is based in Munich and is known for translating high-dimensional scientific problems into robust, production-ready data platforms.
10 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy (PhD), Electrical and Electronics Engineering, Doctor of Philosophy (PhD), Electrical and Electronics Engineering at FAU Erlangen-Nürnberg
Doctor of Philosophy (PhD), Computational Physics, Doctor of Philosophy (PhD), Computational Physics at Ludwig-Maximilians-Universität München
Master of Science (MSc), Physics, Master of Science (MSc), Physics at University of Augsburg
Contributions:1 release, 13 reviews, 329 commits in 4 years 11 months
Contributions summary:Alex's primary contributions center around the development and maintenance of the `anndata` Python library. They have focused on implementing core functionalities like data storage and retrieval, and adding new features, such as the `layers` and `raw` attributes. The user also implemented a system for handling views of the AnnData object and ensuring consistent type handling throughout the codebase. Their work involved modifying existing data structures and adding new features to better serve the scientific community, with specific emphasis on working with sparse matrices.
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