Corneel Casert is a Machine Learning Engineer in the Data and AI Services group at NERSC. His main interests are in the intersection of machine learning and statistical mechanics. This includes using neural-network protocols to control physical systems, designing thermodynamic computers for more energy-efficient machine learning, and training neural networks with Monte-Carlo based methods.

Corneel was a postdoctoral fellow at The Molecular Foundry, working on neural-network based control and optimization for nonequilibrium processes in simulation and experiment. Corneel received a PhD in physics from Ghent University, where he studied fundamental problems in the physics of dynamical and nonequilibrium systems through the application of machine-learning methods.

In Corneel’s spare time, he enjoys baking, birding, hiking, and reading.