Carlo Leonardi

Developing an AI system that delivers multimodal predictions for heart failure patients by integrating heterogeneous data—genetic sequences, CPET biosignals, and clinical reports—using cutting-edge techniques like Large Language Models, LSTMs, and Genomic Language Models in Python. Additionally, I analyze scRNAseq, RNAseq, spatial transcriptomics, and EMseq data for immune-oncology and heart biology projects using both R and Python, deploying solutions with Docker and Singularity on HPC clusters. I have also evaluated and benchmarked topological data analysis methods for single-cell omics, while providing statistical expertise and neural network architecture consultancy for ML, DL, and AI applications in biology.

Pitch presentations, fundraising efforts, and B2B business consultancy to commercialize patentable outcomes from Horizon 2020 consortiums, including FutureAgriculture and Sinfonia. Projects focused on advancements in Synthetic Biology and Organic Chemistry.