Summary
Silvia Cascianelli is a PhD candidate at Politecnico di Milano with 11 years of experience developing computational techniques for genomic and oncogenomic applications. With a Master's in Computer Engineering focused on Data Science and Bioinformatics, she builds machine learning models and robust workflows to analyze transcriptional and mutational NGS data from cancer patients. Her research emphasizes clinically relevant patient stratification—both single- and multi-label—and the identification of predictive genes, variants, and associations with drug sensitivity and genetic risk. Since 2020 she has been a teaching assistant for Bioinformatics and Computational Biology across Politecnico di Milano and Università degli Studi di Milano, blending research with hands-on training. She brings a pragmatic, reproducibility-first approach to translational bioinformatics, often integrating data analysis, ML, and workflow engineering to move discoveries closer to clinical utility.
11 years of coding experience