Arjav Shah is a PhD candidate whose research focuses on the development of sterility testing methods for cell and gene therapies for human disease. Specifically, Arjav is working on new strategies to make biomanufacturing safer through the real-time detection of adventitious agents, such as viruses, at various points in the manufacturing process. With the support of a MathWorks Fellowship, Arjav is pursuing a highly promising novel application of nanopore sensors to fingerprint viruses. The overarching goal of his project is to develop a machine-learning (ML) algorithm that identifies a biomolecule based on its ionic current signature as it is pushed through a nanopore in a freestanding membrane between two electrolyte-filled reservoirs. Arjav’s research employs a combination of multi-scale simulations and ML-driven analysis of experimental data. MATLAB is a vital tool in this work, from model formulation to evaluation and optimization. If successful, Arjav’s nanopore sensor could enhance safety and efficacy in the development of therapeutics for a broad range of diseases and reduce the time required for sterility testing from 2-4 weeks to just hours. His work is also generating innovative new MATLAB tools that are a valuable resource for the nanopore research community.