Despite the success and ambition of ML and AI solutions, data-driven techniques remain rather “dumb”: Whereas a child can distinguish a horse from a cow based on few examples, a typical computer vision system may require tens of thousands of examples. Artificial networks are highly task specific, have difficulty generalizing, and are now bigger than the number of biological neurons directly involved in specific tasks, such as learning new words. Beyond structure, training methods may affect networks’ capabilities.

Alongside Georgetown University cognitive scientists, we are exploring the gap between machine and human intelligence. With data from human brain activity signals, we aim to understand the sequence of events that enable a person to learn and translate these insights into new brain-inspired training algorithms for artificial neural networks.

This project is an excellent example of diverse teams combining the latest in fundamental ML to explore radically new directions with high-impact applications.

example data of normal and abnormal arrhythmia classification

DL Model for Clinical Data

This Nature paper describes DDxNet, a deep learning model for automatic interpretation of electronic health records, ECGs, and EEGs.

model of the structural connectivity of the human brain

TRACK-TBI and the DOE Mission

Understanding the impact of a brain injury and its possible treatments will push the boundaries and capabilities of scientific ML at national labs.

molecular structures drawn on a whiteboard

Advancing Healthcare with Data Science

Data scientists work with domain scientists at LLNL to address major challenges in healthcare including COVID-19.

Jay presenting his research in front of an audience

Meet a Data Science Expert

Jay Thiagarajan's research involves different types of large-scale, structured data that require the design of unique ML techniques.