Background

Healthcare is at a crossroads with enhanced access to clinical, imaging, and genetic data meeting up with rapid advances in artificial intelligence and machine learning. Integration of this data and technology will open new avenues for translating research discoveries into tangible advancements in clinical practice, facilitating tailored care for individual patients. As a Senior Data Science Analyst within the Mathematical Neuro-Oncology Lab and Precision Neurotherapeutics Program, I collaborate closely with a multidisciplinary team comprising mathematicians, data scientists, and physicians. My role revolves around harnessing AI and ML techniques to search for novel biomarkers and interrogate the impact of therapies on patient outcomes.

Currently, I lead efforts in data wrangling to prepare diverse glioma datasets and evaluate model outcomes to extract meaningful insights. In addition, I have worked closely with team members training deep learning models that can segment regions of glioma from magnetic resonance imaging (MRI) and automatically organize MRIs by their imaging type. Concurrently, I oversee the management of local computational resources for the MNO lab, optimizing CPU and GPU-based learning environments to facilitate efficient data processing and model training. I have a strong interest in developing models and systems that produce reproducible output and are accurate for use across institutions. My commitment to improving healthcare outcomes for patients with neurological disorders also fuels translational interests, aiming for seamless integration of AI tools into clinical workflows.