Computational Scientist specializing in AI-driven analysis of biological data, single-cell phenotyping, high-content screening, and bioimage analysis.
My work sits at the intersection of perturbation biology, computer vision, machine learning, and high-performance computing, where I develop scalable methods to quantify cellular responses and extract biologically meaningful phenotypes from large-scale microscopy datasets.
- AI/ML for biological and biomedical data
- Single-cell analysis and phenotypic profiling
- High-content screening and perturbation assays
- Representation learning and foundation models
- Computer vision for microscopy
- Multi-modal biological data integration
- Reproducible scientific computing and HPC workflows
- Python
- PyTorch
- TensorFlow
- Scikit-learn
- Nextflow
- Slurm
- CUDA / OpenCL
- Docker
- Git
Developing AI-powered workflows for large-scale microscopy analysis, enabling researchers to leverage GPU and HPC infrastructure for scalable, reproducible biological discovery.
- Email: alberto.d.sanchez@ntnu.no
- LinkedIn: linkedin.com/in/adiezbiotech


