PhD Candidate @AI4Health UK Research Innovation

Enhanced drug discovery processes by developing advanced machine learning models to predict mechanisms of action and target profiles for potential drug candidates. Designed a robust classification model that accurately identified mechanisms and targets, streamlining early-stage drug development. Additionally, created an innovative model leveraging multi-electrode array (MEA) data to assess drug similarity, with a focus on neuronal effects, providing critical insights into compound behavior at the cellular level.

• Developed a finite element analysis model using Solidworks Simulation to assess the container closure integrity of diverse pre-filled syringes at various temperatures. This aimed to identify the optimal container ensuring sterility during vaccine shipment and distribution.• Built advanced statistical Monte-Carlo simulation models to determine with utmost precision manufacturing inputs parameters for pre-filled syringes. These include determining the optimal fill volume to deliver the safest efficient dose to patients, as well as establishing the plunger insertion depth crucial for maintaining container closure integrity during aerial transport.• Led multiple studies on the RSV vaccine to ensure that process stresses in manufacturing and distribution such as shipping stress, light exposure stress and freeze-thaw stress did not compromise the critical quality attributes of the drug product. Statistically analyzed this data using JMP to identify potential combinations of stresses that could significantly impact the drug product.

As part of an undergraduate research opportunity at Imperial College London, my team and I developed a reusable cannula designed for medical training purposes. I was responsible for designing the cannula using SolidWorks and producing the prototype through 3D printing.