Machine learning engineering is a branch of engineering that implements ongoing data science developments to address complicated or intricate problems with large amounts of disparate data to create cost-effective and far-reaching benefits.

Exascale computers are the world’s fastest supercomputers. They hold a performance of at least one exaflop, or one quintillion calculations per second. Combined with simulation, they are positioned to help tackle some of the world’s greatest challenges, which pertain to topics such as national security, climate, medicine, energy, and water.

At the intersection between quantum mechanics and computer and information science lies at quantum information science (QIS). QIS seeks to understand how information is processed and transmitted using quantum mechanical principles. It is the merger of quantum mechanics and information and computation theory. QIS comprises four major areas: quantum computing, quantum communication, quantum sensing, and quantum foundational science.

Advanced computing testbeds, the proving grounds for new machines, are central to the development of next-generation computers. They allow researchers to explore a complex and non-linear design space and facilitate the evaluation of new computing technologies in terms of performance and efficiency on critical scientific workloads. These “laboratories of machines,” in which multiple components are available for experimentation, are critical to the next greatest advancements in computation.

Graph analytics is the evaluation of information that has been organized as objects and their connections. The purpose of graph analytics is to understand how the objects relate or could relate.

Physics-informed machine learning allows scientists to use this prior knowledge to help the training of the neural network, making it more efficient.

Deep reinforcement learning can best be explained as a method to learn to make a series of good decisions over some time.