Machine learning engineer and data scientist specializing in computational biology, computer vision, and translational research. Currently completing an industry PhD at ETH Zurich in collaboration with IBM Research Zurich and the University Hospital Lausanne, developing computer-vision methods for spatial omics data in cancer research. Passionate about bridging machine learning and biomedical discovery while building reproducible, open-source tools.

• Machine Learning & Data Science: Computer Vision · Self-Supervised Learning · Representation Learning · Multi-Modal Modeling · Statistical Analysis
• Tooling & Engineering: PyTorch · Lightning · scikit-learn · TensorFlow · Git · Snakemake · Ray · Docker · FastAPI · Azure ML Studio · Azure DevOps · W&B
• Domain Expertise: Spatial Omics · Single-Cell Analysis · Digital Pathology · Drug Discovery Workflows
• Languages: German (native) · English (fluent) · French (beginner) · Spanish (beginner)

• ATHENA — Python toolkit for representation learning and statistical analysis of spatial single-cell data.
• SpatialProteomicsNet — Unified data access layer for spatial omics datasets.
• DEL-Hit — Framework for analyzing DNA-encoded chemical libraries with high-throughput performance.

➡️ Explore the full list on GitHub.

• A. Martinelli, F. Bonollo, S. Karkampouna, et al.
  Cellular and molecular profiling of the prostate cancer microenvironment. In preparation, 2025.

• A. Martinelli, A. Lessing, G. Hoppeler, et al.
  DEL-Hit: a computational framework for DNA-encoded libraries. In preparation, 2025.

• A. Martinelli, M. Rapsomaniki.
  SpatialProteomicsNet: unified access to spatial proteomics datasets. Journal of Open Source Software (under review), 2025.

• P. Pati, S. Karkampouna, F. Bonollo, ..., A. Martinelli, et al.
  Accelerating histopathology workflows with generative AI-based virtually multiplexed tumour profiling. Nature Machine Intelligence, 2024.

• M. Keller, D. Petrov, A. Glogger, ..., A. Martinelli, et al.
  Highly pure DNA-encoded chemical libraries by dual-linker solid-phase synthesis. Science, 2023.

• A. Martinelli, M. A. Rapsomaniki.
  ATHENA: analysis of tumor heterogeneity from spatial omics measurements. Bioinformatics, 2022.

• A. Martinelli, J. Wagner, B. Bodenmiller, et al.
  scQUEST: scQUEST: Quantifying tumor ecosystem heterogeneity from mass or flow cytometry data. STAR Protocols, 2022.

🔍 See more on Google Scholar.