Research Overview
The TENSOR Lab represents a cutting-edge intersection of machine learning and health, focusing on both developing novel methodologies and applying existing models to solve complex health-related issues. This innovative lab is structured around two primary pillars: the exploration and creation of new machine learning algorithms and techniques, and the practical application of these developments to address real-world health challenges.
Research Areas
Reinforcement Learning
Reinforcement learning is a type of machine learning in which an agent learns to make sequential decisions by interacting with an environment and receiving feedback in the form of rewards to maximize its cumulative return.
Computer Vision
The intersection of classic image processing and machine learning to derive inferences from medical imaging data.
Multimodal AI
Using disparate data streams to facilitate artificial intelligence to make decisions using multifaceted data akin to a clinician
Natural Language Processing
Using text and speech data in the context of machine learning to offer a wide range of applications that enhance both patient care and healthcare operations