Resources

A growing collection of books, documentation, and tools related to my research. The links point to official project pages or learning materials.

Machine learning

• Dive into Deep Learning — an interactive introduction to deep learning, combining mathematical explanations and code.
• Probabilistic Machine Learning — a reference for probabilistic modeling and machine learning.
• PyTorch documentation — reference documentation for models, tensors, and training workflows.

Protein modeling & engineering

• ProteinGym — resources for evaluating protein fitness prediction.
• ESM — protein language models and associated tools.
• ProteinMPNN — protein sequence design conditioned on structure.
• Rosetta — software and resources for biomolecular modeling.

Single-cell & computational biology

• Scanpy — tools for analyzing single-cell gene expression data.
• scverse — an ecosystem of tools for single-cell analysis.
• Biopython — Python tools for biological computation.

Mathematics & statistics

Useful foundations for the topics above include linear algebra, probability, statistical inference, and optimization. I will add specific reading notes as this collection grows.

Research software

• Python documentation — language and standard-library reference.
• R Project — statistical computing and graphics.
• Git documentation — version control and collaborative development.