From February 6 through March 26, 2024, the ALCF will host an 8-part weekly virtual training series to teach undergraduates and graduates the fundamentals of using world-class supercomputers to advance the use of AI for research.
Trainees will learn advanced topics in convolutional neural networks, such as deep, residual, variational, and adversarial networks
Corey Adams is a Computational Scientist at the Argonne Leadership Computing Facility. Originally a high-energy physicist working on neutrino physics problems, he now works on applying deep learning and machine learning techniques to science problems – and still neutrino physics – on high-performance computers. He has experience in classification, segmentation, and sparse convolutional neural networks as well as running machine learning training at scale.
Katerina Vriza is an incoming staff scientist at the Center for Nanoscale Materials at Argonne National Lab. Her main focus is on using AI/ML to extract scientific data from literature and using such data and ML and robotics for the design and synthesis of polymer materials. She works on an autonomous materials synthesis robot called Polybot. Katerina is also the current lead developer for EXSCLAIM, a code that extracts and curates labeled electron microscopy datasets using AI. She will speak about Extracting and processing multimodal data from literature to guide robotic experiments.