Rachel’s main focus centers in multimodal, translational machine learning in complex systems and brain sciences, where she is receiving her doctor’s in philosophy. Her background in both medicine and biology helps structure the integration of machine learning models for both academia and industry applications. Previous work involves a variety of research fields including mental disorder diagnosis, epileptic mice investigations, and synthetic drug detection. Drawing from interdisciplinary experiences drives her current integrative research in deep learning proteomics, cancer imaging, and therapeutic XR platforms. Her future accomplishments aim to include advancements in artificial animal models, microfluidic machine learning, connectomics, and general AI. Besides her highly rewarding work in deep artificial neural networks, Rachel enjoys novel experiences such as travelling and gastronomy.