Machine Learning Methods for Scientific Data Compression

1. Introduction  2. Autoencoders  3. Constrained Autoencoders  4. Guaranteed Autoencoders  5. Adaptive Data Reduction  6. Attention and Hierarchical methods  7. Guaranteed Conditional Diffusion  8. Foundation Models

Biography

Xiao Li is a Ph.D. student at the University of Florida, specializing in machine learning for scientific data reduction, large language models, generative AI, and AI for science. He holds M.S.E. and B.S. degrees from Sun Yat-sen University.

Jaemoon Lee is a postdoctoral associate at Oak Ridge National Laboratory. He earned his Ph.D. and M.S. from the University of Florida, focusing on machine learning, physics-informed neural networks, large language models, and data compression.

Tania Banerjee, Ph.D., is an Assistant Professor at the University of Houston. Her research integrates high-performance computing with AI and ML for data-driven solutions in transportation, healthcare, cybersecurity, and large-scale scientific data compression.

Liangji Zhu is a Ph.D. student at the University of Florida. His research areas include machine learning for predictive analytics, scientific data compression, generative AI, spatiotemporal modeling, and AI for science.

Qian Gong is a computer scientist at Oak Ridge National Laboratory. With a Ph.D. from Duke University, her research interests encompass lossy compression, data management, and AI-based surrogate modeling for scientific applications.

Scott Klasky is a Distinguished Scientist at Oak Ridge National Laboratory, leading efforts in high-performance data management and data reduction for scientific computing. He founded ADIOS and developed MGARD.

Rahul Sengupta, Ph.D., is an Adjunct Research Scientist at the University of Florida. His research applies machine learning models to sequential and time-series data, particularly in transportation engineering.

Anand Rangarajan is a Professor at the University of Florida, specializing in machine learning, computer vision, medical and hyperspectral imaging, and the science of consciousness.

Sanjay Ranka is a Distinguished Professor at the University of Florida. His research focuses on high-performance computing and big data science, with applications in CFD, healthcare, and transportation. He is a Fellow of IEEE and AAAS.