Machine Learning Scientist, Weill Cornell Medicine & Tempus Labs
Akshay Goel is currently a machine learning scientist at Tempus Labs and an associate researcher at Weill Cornell Medicine. He received his undergraduate degree in computer science from Carnegie Mellon University and his medical degree from Rutgers University. He subsequently completed his radiology residency at Columbia-New York-Presbyterian Hospital and an MRI fellowship at Well Cornell Medicine. Dr. Goel leads the development and deployment of multiple end-to-end machine learning pipelines, which have been validated with real-world clinical deployment.
Data Centric AI and Deploying a Polycystic Kidney Disease Model
Data is an essential component for the real-world deployment of machine learning (ML) pipelines. Generating high-quality data, combined with state-of-the-art vision models enables small teams to train, validate and deploy ML with performance suitable for controlled prospective deployment. We will review the key considerations for building an end-to-end ML pipeline for medical imaging, including the model-data complexities, regulatory considerations, and technical constraints.