Clinical Pharmacology, Modeling and Simulation

Leveraged my expertise in advanced machine learning techniques and mathematical modeling to support a broad range of areas within clinical trials. My work in this capacity required me to collaborate effectively with cross-functional teams, including colleagues in clinical trial management and supply chain management.My contributions included developing models that provide valuable insights and predictions to support clinical trial planning and execution, including probabilistic modeling, machine learning, and natural language processing.

Built machine learning models to predict the default rate of contracts. The work in this capacity involved a binary classification challenge, with the goal of accurately predicting whether a contract would default or not.

Developed an innovative deep learning model to predict protein interfaces within the Multiscale 3D Human Interactome Network. I employed a range of advanced deep learning techniques such as recurrent neural networks (RNN), convolutional neural networks (CNN), and graph convolutional networks (GCN) to create this model.Additionally, I developed and implemented a comprehensive pipeline for the deep learning model on the lab server, which involved data preprocessing, training the model, and evaluating its performance. The work resulted in achieving the best performance at the time of development.