Nicholas Luongo

Nicholas Luongo, Manager of Computational and Scientific Programming, helps researchers tailor their scientific and computing approaches to make the best use of Empire AI resources. This involves training users in HPC concepts, developing tools and workflows for them to use, and understanding their projects to enhance scientific output.

Nicholas applies AI/ML and high-performance computing in concert to further scientific research. He holds a PhD in Physics from the University of Oregon and previously worked at Argonne National Laboratory in the High Energy Physics division.

During his research, he analyzed data from the ATLAS particle physics experiment to search for dark matter and other undiscovered physics phenomena. This involved building machine learning models and scaling their training on HPC resources to process large amounts of experimental data. He has expertise in deep learning, foundation models, and anomaly detection.

As Empire AI continues to expand its computing capacity and research community, building the team behind that infrastructure is essential to helping researchers make the most of these extraordinary resources.