Intro to AI Series: Advanced Topics in Neural Networks
From October 1 - November 12, 2024, the ALCF will host a 7-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.
Intro to AI Series: Session 3
Trainees will learn advanced topics in convolutional neural networks, such as deep, residual, variational, and adversarial networks
Lecturer
Bethany Lusch is a Computer Scientist in the data science group at the Argonne Leadership Computing Facility at Argonne National Lab. Her research expertise includes developing methods and tools to integrate AI with science, especially for dynamical systems and PDE-based simulations. Her recent work includes developing machine-learning emulators to replace expensive parts of simulations, such as computational fluid dynamics simulations of engines and climate simulations. She is also working on methods that incorporate domain knowledge in machine learning, representation learning, and using machine learning to analyze supercomputer logs. She holds a Ph.D. and MS in applied mathematics from the University of Washington and a BS in mathematics from the University of Notre Dame.
AI for Science Talk Speaker
Nesar Ramachandra is a cosmologist with interests in the dynamics of large-scale structure formation; he is also working on the implementation of state of the art statistical and machine learning methods for cosmological data analysis and fast prediction tools (emulators) as part of the SciDAC-4 project led by CPAC. In his talk, he will speak about AI for Cosmology.